Dietary intake, Nutritional status, and Health outcomes among Vegan, Vegetarian and Omnivore families: results from the observational study
Statistical report - mixed-effects models in adults
Authors and affiliations
Marina Heniková1,2, Anna Ouřadová1, Eliška Selinger1,3, Filip Tichanek4, Petra Polakovičová4, Dana Hrnčířová2, Pavel Dlouhý2, Martin Světnička5, Eva El-Lababidi5, Jana Potočková1, Tilman Kühn6, Monika Cahová4, Jan Gojda1
1 Department of Internal Medicine, Kralovske Vinohrady University Hospital and Third Faculty of Medicine, Charles University, Prague, Czech Republic.
2 Department of Hygiene, Third Faculty of Medicine, Charles University, Prague, Czech Republic.
3 National Health Institute, Prague, Czech Republic.
4 Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
5 Department of Pediatrics, Kralovske Vinohrady University Hospital and Third Faculty of Medicine, Charles University, Prague, Czech Republic.
6 Department of Epidemiology, MedUni, Vienna, Austria.
This is a statistical report of the study currenlty under review in the Communications Medicine journal.
When using this code or data, cite the original publication:
TO BE ADDED
BibTex citation for the original publication:
TO BE ADDED
Original GitHub repository: https://github.com/filip-tichanek/kompas_clinical
Statistical reports can be found on the reports hub.
Data analysis is described in detail in the statistical methods report.
1 Introduction
This project is designed to evaluate and compare clinical outcomes across three distinct dietary strategy groups:
- Vegans
- Vegetarians
- Omnivores
The dataset includes both adults and children, with data clustered within families.
1.1 Main Questions
The study addresses the following key questions:
Q1. Do clinical outcomes vary significantly across different diet strategies?
Q2. Beyond diet group, which factors (e.g., sex, age, breastfeeding status for children, or supplementation when applicable) most strongly influence clinical outcomes? How correlated (“clustered”) are these characteristics within the same family?
Q3. Could the clinical characteristics effectively discriminate between different diet groups?
1.2 Statistical Methods
For full methodological details, see this report. In brief:
Robust linear mixed-effects models (rLME) were used to estimate adjusted differences between diet groups (Q1) and assess the importance of other variables (Q2), including how much clinical characteristics tend to cluster within families. Covariates included age, sex, breastfeeding status for children, and relevant supplementation factors where applicable.
Elastic net logistic regression was employed to answer Q3, evaluating whether clinical characteristics provide a strong overall signal distinguishing between diet groups, incorporating a predictive perspective.
All analyses were conducted separately for adults and children.
2 Analysis
2.1 Import initiation file
Open code
getwd()
## [1] "/home/ticf/GitRepo/ticf/368_MOCA_kompas_clinical"
setwd('/home/ticf/GitRepo/ticf/368_MOCA_kompas_clinical/')
source('r/368_initiation.R')2.2 Mixed models
Adults models will be adjusted for the effect of aAGE, SEX. Relevant supplementation for vitamins and biogenic elements will be added as covariates.
Open code
AIC_adult <- data.frame(outcome = NA,
estimand = NA,
aAGE = NA,
aSEX = NA,
other_cov = NA,
diet = NA,
family = NA)
diet_adult <- data.frame(outcome = NA,
estimand = NA,
VN_OM_diff = NA,
VN_OM_P = NA,
VG_OM_diff = NA,
VG_OM_P = NA,
VN_VG_diff = NA,
VN_VG_P = NA)
diet_adult_non_robust <- data.frame(outcome = NA,
estimand = NA,
VN_OM_diff = NA,
VN_OM_P = NA,
VG_OM_diff = NA,
VG_OM_P = NA,
VN_VG_diff = NA,
VN_VG_P = NA)
i = 1
j = 1
ni= 52.2.1 aBMI
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aBMI"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.6583 0.5744 41.186 < 2e-16 ***
## SEXM 1.8660 0.4802 3.886 0.00015 ***
## GRPVG -0.3709 0.7635 -0.486 0.62784
## GRPVN -1.2684 0.6610 -1.919 0.05682 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.518 0.4727
## s(FAM) 25.56 92 0.381 0.0421 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.231 Deviance explained = 35.3%
## GCV = 11.843 Scale est. = 9.9077 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 15.100 0.00015
## GRP 2 2.073 0.12931
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.518 0.4727
## s(FAM) 25.56 92.00 0.381 0.0421
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.54884 0.03350 135.802 < 2e-16 ***
## SEXM 0.11627 0.02841 4.092 6.77e-05 ***
## GRPVG -0.02827 0.04439 -0.637 0.5252
## GRPVN -0.07795 0.03844 -2.028 0.0443 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.687 0.4086
## s(FAM) 23.09 92 0.333 0.0605 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.226 Deviance explained = 33.8%
## GCV = 0.04091 Scale est. = 0.034766 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 16.748 6.77e-05
## GRP 2 2.219 0.112
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.687 0.4086
## s(FAM) 23.09 92.00 0.333 0.0605
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 22.24062674 1.73837238 12.7939370
## GRPVG -0.88003943 0.67090599 -1.3117180
## GRPVN -1.31850756 0.58158915 -2.2670773
## SEXM 2.00007815 0.49297263 4.0571789
## aAGE 0.03294712 0.04700117 0.7009852
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on body mass index, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -1.3185076 | -2.458401 | -0.1786138 | 0.0233855 |
| VG vs OM | -0.8800394 | -2.194991 | 0.4349121 | 0.1896153 |
| VN vs VG | -0.4384681 | -1.615560 | 0.7386238 | 0.4653359 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.145
## Unadjusted ICC: 0.129
i = i+12.2.2 aWHR
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aWHR"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7556953 0.0087453 86.411 <2e-16 ***
## SEXM 0.0806633 0.0078054 10.334 <2e-16 ***
## GRPVG 0.0007818 0.0114524 0.068 0.946
## GRPVN -0.0153586 0.0099812 -1.539 0.126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 4.795 0.030 *
## s(FAM) 14.55 92 0.190 0.158
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.455 Deviance explained = 51%
## GCV = 0.0029065 Scale est. = 0.0025959 n = 183
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 106.80 <2e-16
## GRP 2 1.77 0.174
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 4.795 0.030
## s(FAM) 14.55 92.00 0.190 0.158
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 0.6927888286 0.0290469719 23.85063854
## GRPVG 0.0002553297 0.0111566478 0.02288588
## GRPVN -0.0141290444 0.0097355910 -1.45127752
## SEXM 0.0784653996 0.0082660173 9.49252783
## aAGE 0.0017007427 0.0007845351 2.16783504
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on waist-to-hip ratio, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.0141290 | -0.0332105 | 0.0049524 | 0.1467026 |
| VG vs OM | 0.0002553 | -0.0216113 | 0.0221220 | 0.9817413 |
| VN vs VG | -0.0143844 | -0.0340846 | 0.0053159 | 0.1524037 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.080
## Unadjusted ICC: 0.047
i = i+12.2.3 aHEIGHT
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aHEIGHT"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.690240 0.011812 143.094 <2e-16 ***
## SEXM 0.116140 0.008851 13.122 <2e-16 ***
## GRPVG 0.007771 0.016002 0.486 0.628
## GRPVN -0.005107 0.013838 -0.369 0.713
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.001 1.001 0.487 0.486325
## s(FAM) 41.309 92.000 0.816 0.000701 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.594 Deviance explained = 69.3%
## GCV = 0.0043899 Scale est. = 0.0033027 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 172.188 <2e-16
## GRP 2 0.409 0.665
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.001 1.001 0.487 0.486325
## s(FAM) 41.309 92.000 0.816 0.000701
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 1.6668943452 0.038471017 43.3285749
## GRPVG 0.0046990382 0.016304026 0.2882134
## GRPVN -0.0078189767 0.014100503 -0.5545176
## SEXM 0.1159475712 0.009069895 12.7837833
## aAGE 0.0006805378 0.001036233 0.6567421
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on body height, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.007819 | -0.0354555 | 0.0198175 | 0.5792247 |
| VG vs OM | 0.004699 | -0.0272563 | 0.0366543 | 0.7731834 |
| VN vs VG | -0.012518 | -0.0410229 | 0.0159869 | 0.3893896 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.280
## Unadjusted ICC: 0.157
i = i+12.2.4 aMASS
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aMASS"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 67.7873 1.9041 35.600 <2e-16 ***
## SEXM 15.6073 1.5450 10.102 <2e-16 ***
## GRPVG -0.6507 2.5454 -0.256 0.7986
## GRPVN -4.4511 2.2031 -2.020 0.0451 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 1.068 0.3031
## s(FAM) 30.34 92 0.496 0.0133 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.477 Deviance explained = 57.4%
## GCV = 125.83 Scale est. = 102.05 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 102.044 <2e-16
## GRP 2 2.565 0.0803
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 1.068 0.3031
## s(FAM) 30.34 92.00 0.496 0.0133
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.06078 0.03621 167.379 <2e-16 ***
## SEXM 0.30900 0.02999 10.305 <2e-16 ***
## GRPVG -0.01414 0.04822 -0.293 0.7697
## GRPVN -0.08683 0.04174 -2.080 0.0392 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 1.282 0.2593
## s(FAM) 27.11 92 0.420 0.0267 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.474 Deviance explained = 56.2%
## GCV = 0.046573 Scale est. = 0.038575 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 106.183 <2e-16
## GRP 2 2.681 0.0717
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 1.282 0.2593
## s(FAM) 27.11 92.00 0.420 0.0267
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 60.9689641 6.0073255 10.149103
## GRPVG -1.3429717 2.3184622 -0.579251
## GRPVN -3.9016422 2.0098083 -1.941301
## SEXM 15.3633452 1.7035746 9.018299
## aAGE 0.1710179 0.1624228 1.052918
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on body mass, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -3.901642 | -7.840794 | 0.0375097 | 0.0522218 |
| VG vs OM | -1.342972 | -5.887074 | 3.2011308 | 0.5624198 |
| VN vs VG | -2.558671 | -6.626369 | 1.5090278 | 0.2176280 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.191
## Unadjusted ICC: 0.122
i = i+12.2.5 aHG
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aHG"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30.8431 1.1568 26.661 <2e-16 ***
## SEXM 21.6728 0.9280 23.354 <2e-16 ***
## GRPVG 1.1900 1.5486 0.768 0.443
## GRPVN 0.6024 1.3318 0.452 0.652
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 3.915 4.768 1.981 0.0935 .
## s(FAM) 30.549 92.000 0.504 0.0113 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.785 Deviance explained = 82.9%
## GCV = 45.608 Scale est. = 36.176 n = 186
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 545.427 <2e-16
## GRP 2 0.296 0.744
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 3.915 4.768 1.981 0.0935
## s(FAM) 30.549 92.000 0.504 0.0113
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 26.3038005 3.68630971 7.1355373
## GRPVG 1.0621994 1.42147922 0.7472493
## GRPVN 0.5192798 1.23566193 0.4202443
## SEXM 21.8339180 1.04094982 20.9749957
## aAGE 0.1249945 0.09928477 1.2589494
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on hand grip, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.5192798 | -1.902573 | 2.941133 | 0.6743070 |
| VG vs OM | 1.0621994 | -1.723849 | 3.848247 | 0.4549131 |
| VN vs VG | -0.5429196 | -3.024613 | 1.938774 | 0.6680827 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.214
## Unadjusted ICC: 0.059
i = i+12.2.6 aPFAT
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aPFAT"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.40511 1.09643 25.907 <2e-16 ***
## SEXM -10.44955 0.89986 -11.612 <2e-16 ***
## GRPVG 0.03024 1.46278 0.021 0.984
## GRPVN -1.59845 1.26604 -1.263 0.209
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.054 1.098 2.118 0.1558
## s(FAM) 28.558 92.000 0.450 0.0212 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.494 Deviance explained = 58.3%
## GCV = 42.279 Scale est. = 34.68 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 134.848 <2e-16
## GRP 2 1.163 0.315
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.054 1.098 2.118 0.1558
## s(FAM) 28.558 92.000 0.450 0.0212
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
With log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.81850 0.07924 60.809 <2e-16 ***
## SEXM -0.74686 0.06880 -10.856 <2e-16 ***
## GRPVG -0.03273 0.10450 -0.313 0.754
## GRPVN -0.13062 0.09050 -1.443 0.151
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 2.447 0.1197
## s(FAM) 18.87 92 0.259 0.0976 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.437 Deviance explained = 50.7%
## GCV = 0.2346 Scale est. = 0.20466 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 117.853 <2e-16
## GRP 2 1.206 0.302
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 2.447 0.1197
## s(FAM) 18.87 92.00 0.259 0.0976
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Did not improve the fit much, lets continue to work with original scale.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 23.30037996 3.57040791 6.52597142
## GRPVG -0.02096941 1.37796025 -0.01521772
## GRPVN -1.80329491 1.19451420 -1.50964711
## SEXM -10.40427980 1.01250653 -10.27576563
## aAGE 0.13892100 0.09653475 1.43907759
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on body fat percentage, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -1.8032949 | -4.144500 | 0.5379099 | 0.1311335 |
| VG vs OM | -0.0209694 | -2.721722 | 2.6797831 | 0.9878585 |
| VN vs VG | -1.7823255 | -4.199931 | 0.6352799 | 0.1484748 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.173
## Unadjusted ICC: 0.105
i = i+12.2.7 aFAT
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aFAT"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.6876 1.2280 16.032 < 2e-16 ***
## SEXM -4.1947 0.9706 -4.322 2.83e-05 ***
## GRPVG 0.3310 1.6493 0.201 0.841
## GRPVN -2.2105 1.4271 -1.549 0.124
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 1.268 0.2620
## s(FAM) 34.23 92 0.595 0.0056 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.295 Deviance explained = 44%
## GCV = 50.73 Scale est. = 40.087 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 18.679 2.83e-05
## GRP 2 1.994 0.14
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 1.268 0.2620
## s(FAM) 34.23 92.00 0.595 0.0056
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
With log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.23636 0.10822 39.147 < 2e-16 ***
## SEXM -0.43864 0.09205 -4.765 4.22e-06 ***
## GRPVG -0.04587 0.14333 -0.320 0.7494
## GRPVN -0.21824 0.12411 -1.758 0.0806 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 2.054 0.1538
## s(FAM) 22.58 92 0.327 0.0576 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.23 Deviance explained = 34%
## GCV = 0.42832 Scale est. = 0.36515 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 22.706 4.22e-06
## GRP 2 1.845 0.161
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 2.054 0.1538
## s(FAM) 22.58 92.00 0.327 0.0576
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Looks much better after log2-transformation of the outcome variable
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 3.78019421 0.344898009 10.96032484
## GRPVG -0.01257843 0.133109650 -0.09449675
## GRPVN -0.19998709 0.115388935 -1.73315659
## SEXM -0.40721092 0.097807168 -4.16340564
## aAGE 0.01227510 0.009325165 1.31634152
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on log2(body fat weight), estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.1999871 | -0.4261453 | 0.0261711 | 0.0830678 |
| VG vs OM | -0.0125784 | -0.2734686 | 0.2483117 | 0.9247146 |
| VN vs VG | -0.1874087 | -0.4209470 | 0.0461297 | 0.1157601 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.131
## Unadjusted ICC: 0.115
i = i+12.2.8 aFFM
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aFFM"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.0756 1.0018 47.989 <2e-16 ***
## SEXM 19.6387 0.8596 22.846 <2e-16 ***
## GRPVG -0.7113 1.3415 -0.530 0.5967
## GRPVN -2.1366 1.1435 -1.868 0.0636 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 5.119 6.15 1.815 0.106
## s(FAM) 21.618 92.00 0.307 0.067 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.772 Deviance explained = 80.8%
## GCV = 37.123 Scale est. = 31.021 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 521.942 <2e-16
## GRP 2 1.912 0.151
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 5.119 6.150 1.815 0.106
## s(FAM) 21.618 92.000 0.307 0.067
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.57401 0.02412 231.126 <2e-16 ***
## SEXM 0.50059 0.02057 24.342 <2e-16 ***
## GRPVG -0.01057 0.03240 -0.326 0.745
## GRPVN -0.05486 0.02753 -1.993 0.048 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 5.808 6.851 1.990 0.0703 .
## s(FAM) 23.182 92.000 0.336 0.0545 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.796 Deviance explained = 83.1%
## GCV = 0.021288 Scale est. = 0.017532 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 592.524 <2e-16
## GRP 2 2.389 0.0951
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 5.808 6.851 1.990 0.0703
## s(FAM) 23.182 92.000 0.336 0.0545
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Looks much better after the log2-transformation
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 5.524849932 0.079280037 69.6877822
## GRPVG -0.022038004 0.030597271 -0.7202604
## GRPVN -0.056739829 0.026523896 -2.1391966
## SEXM 0.506876553 0.022482461 22.5454217
## aAGE 0.001379116 0.002143531 0.6433854
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on free fat mass, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.0567398 | -0.1087257 | -0.0047539 | 0.0324197 |
| VG vs OM | -0.0220380 | -0.0820076 | 0.0379315 | 0.4713647 |
| VN vs VG | -0.0347018 | -0.0883842 | 0.0189805 | 0.2051632 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.041
## Unadjusted ICC: 0.010
i = i+12.2.9 aSBP
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aSBP"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 119.512 2.392 49.955 < 2e-16 ***
## SEXM 16.287 2.120 7.681 1.26e-12 ***
## GRPVG -3.531 3.140 -1.124 0.2625
## GRPVN -5.005 2.720 -1.840 0.0676 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.659 0.418
## s(FAM) 14.89 92 0.193 0.167
##
## R-sq.(adj) = 0.317 Deviance explained = 38.6%
## GCV = 218.29 Scale est. = 195.07 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 59.002 1.26e-12
## GRP 2 1.702 0.186
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.659 0.418
## s(FAM) 14.89 92.00 0.193 0.167
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 117.17113381 7.8405823 14.9441877
## GRPVG -3.14998169 3.0259878 -1.0409763
## GRPVN -4.13054283 2.6231420 -1.5746547
## SEXM 16.92237840 2.2234548 7.6108487
## aAGE 0.03104981 0.2119894 0.1464687
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on systolic blood pressure, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -4.1305428 | -9.271807 | 1.010721 | 0.1153362 |
| VG vs OM | -3.1499817 | -9.080809 | 2.780845 | 0.2978865 |
| VN vs VG | -0.9805611 | -6.289600 | 4.328478 | 0.7173531 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.074
## Unadjusted ICC: 0.054
i = i+12.2.10 aDBP
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aDBP"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 75.704 1.449 52.243 < 2e-16 ***
## SEXM 3.890 1.288 3.019 0.00293 **
## GRPVG 2.243 1.901 1.180 0.23959
## GRPVN -2.646 1.647 -1.607 0.10999
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.908 0.342
## s(FAM) 14.26 92 0.184 0.171
##
## R-sq.(adj) = 0.166 Deviance explained = 24.8%
## GCV = 80.335 Scale est. = 72.063 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 9.117 0.00293
## GRP 2 4.332 0.01464
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.908 0.342
## s(FAM) 14.26 92.00 0.184 0.171
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 73.32003007 4.842061 15.1423201
## GRPVG 1.95773935 1.868741 1.0476249
## GRPVN -2.32280995 1.619958 -1.4338707
## SEXM 3.73258466 1.373125 2.7183130
## aAGE 0.06993215 0.130917 0.5341716
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on diastolic blood pressure, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -2.322810 | -5.497869 | 0.8522489 | 0.1516092 |
| VG vs OM | 1.957739 | -1.704925 | 5.6204040 | 0.2948115 |
| VN vs VG | -4.280549 | -7.559220 | -1.0018787 | 0.0105010 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.073
## Unadjusted ICC: 0.065
i = i+12.2.11 aGLY
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aGLY"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.40512 0.07915 55.657 <2e-16 ***
## SEXM 0.17073 0.07066 2.416 0.0168 *
## GRPVG 0.02025 0.10468 0.193 0.8468
## GRPVN -0.07992 0.08973 -0.891 0.3744
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 3.909 4.838 1.078 0.437
## s(FAM) 13.609 92.000 0.175 0.164
##
## R-sq.(adj) = 0.126 Deviance explained = 22.2%
## GCV = 0.24205 Scale est. = 0.21419 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 5.838 0.0168
## GRP 2 0.724 0.4865
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 3.909 4.838 1.078 0.437
## s(FAM) 13.609 92.000 0.175 0.164
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformation of the outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.135374 0.024287 87.921 <2e-16 ***
## SEXM 0.051870 0.021048 2.464 0.0148 *
## GRPVG -0.002301 0.032364 -0.071 0.9434
## GRPVN -0.026463 0.027703 -0.955 0.3409
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 4.236 5.187 1.162 0.3344
## s(FAM) 19.404 92.000 0.271 0.0784 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.167 Deviance explained = 28.7%
## GCV = 0.022115 Scale est. = 0.018846 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 6.073 0.0148
## GRP 2 0.599 0.5504
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 4.236 5.187 1.162 0.3344
## s(FAM) 19.404 92.000 0.271 0.0784
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 4.330043507 0.215561743 20.0872541
## GRPVG -0.073920973 0.083193720 -0.8885403
## GRPVN -0.085287078 0.072118248 -1.1826005
## SEXM 0.146823594 0.061129618 2.4018405
## aAGE 0.002988749 0.005828241 0.5128046
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on glucose level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.0852871 | -0.2266362 | 0.0560621 | 0.2369675 |
| VG vs OM | -0.0739210 | -0.2369777 | 0.0891357 | 0.3742502 |
| VN vs VG | -0.0113661 | -0.1573279 | 0.1345957 | 0.8786954 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.082
## Unadjusted ICC: 0.078
i = i+12.2.12 aTC
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aTC"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.9507 0.1405 35.244 < 2e-16 ***
## SEXM -0.2303 0.1277 -1.803 0.073102 .
## GRPVG -0.3664 0.1833 -1.999 0.047131 *
## GRPVN -0.5887 0.1588 -3.707 0.000282 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000 1 2.166 0.143
## s(FAM) 9.587 92 0.117 0.262
##
## R-sq.(adj) = 0.135 Deviance explained = 19.8%
## GCV = 0.77105 Scale est. = 0.7109 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 3.252 0.07310
## GRP 2 6.872 0.00134
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000 1.000 2.166 0.143
## s(FAM) 9.587 92.000 0.117 0.262
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformation of the outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.30210 0.04387 52.480 < 2e-16 ***
## SEXM -0.08842 0.04005 -2.208 0.0286 *
## GRPVG -0.12997 0.05717 -2.273 0.0242 *
## GRPVN -0.20140 0.04954 -4.065 7.26e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000 1 3.385 0.0675 .
## s(FAM) 8.723 92 0.105 0.2809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.154 Deviance explained = 21.2%
## GCV = 0.075468 Scale est. = 0.06993 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 4.874 0.028574
## GRP 2 8.265 0.000373
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000 1.000 3.385 0.0675
## s(FAM) 8.723 92.000 0.105 0.2809
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 2.01221746 0.147841153 13.610672
## GRPVG -0.14594911 0.057057691 -2.557922
## GRPVN -0.20523119 0.049461675 -4.149297
## SEXM -0.08209545 0.041925219 -1.958140
## aAGE 0.00792046 0.003997249 1.981478
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on log2(total cholesterole) level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.2052312 | -0.3021743 | -0.1082881 | 0.0000333 |
| VG vs OM | -0.1459491 | -0.2577801 | -0.0341181 | 0.0105300 |
| VN vs VG | -0.0592821 | -0.1593887 | 0.0408246 | 0.2457763 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.039
## Unadjusted ICC: 0.034
i = i+12.2.13 aHDL
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aHDL"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.74986 0.05750 30.431 <2e-16 ***
## SEXM -0.45602 0.04650 -9.806 <2e-16 ***
## GRPVG -0.07849 0.07697 -1.020 0.309
## GRPVN -0.03907 0.06657 -0.587 0.558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.328 1.549 3.710 0.0661 .
## s(FAM) 30.824 92.000 0.505 0.0127 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.43 Deviance explained = 53.8%
## GCV = 0.11446 Scale est. = 0.092333 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 96.16 <2e-16
## GRP 2 0.52 0.595
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.328 1.549 3.710 0.0661
## s(FAM) 30.824 92.000 0.505 0.0127
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 1.42352546 0.183953921 7.7384894
## GRPVG -0.07063416 0.070995023 -0.9949170
## GRPVN -0.03658183 0.061543548 -0.5944056
## SEXM -0.42410052 0.052166181 -8.1297981
## aAGE 0.00806825 0.004973646 1.6222002
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on HDL cholesterol level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.0365818 | -0.1572050 | 0.0840413 | 0.5522408 |
| VG vs OM | -0.0706342 | -0.2097818 | 0.0685135 | 0.3197766 |
| VN vs VG | 0.0340523 | -0.0905071 | 0.1586118 | 0.5920835 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.188
## Unadjusted ICC: 0.131
i = i+12.2.14 aLDL
Data selection
Open code
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aLDL"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.80560 0.11210 25.028 < 2e-16 ***
## SEXM 0.05931 0.09950 0.596 0.551924
## GRPVG -0.24289 0.14709 -1.651 0.100548
## GRPVN -0.48219 0.12742 -3.784 0.000214 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.807 0.370
## s(FAM) 14.59 92 0.189 0.162
##
## R-sq.(adj) = 0.157 Deviance explained = 24.1%
## GCV = 0.47997 Scale est. = 0.42969 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 0.355 0.551924
## GRP 2 7.289 0.000922
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.807 0.370
## s(FAM) 14.59 92.00 0.189 0.162
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 2.44318478 0.35753954 6.8333276
## GRPVG -0.31967964 0.13798851 -2.3167120
## GRPVN -0.50090939 0.11961828 -4.1875656
## SEXM 0.02200058 0.10139209 0.2169852
## aAGE 0.01044583 0.00966696 1.0805699
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on LDL cholesterol level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.5009094 | -0.7353569 | -0.2664619 | 0.0000282 |
| VG vs OM | -0.3196796 | -0.5901322 | -0.0492271 | 0.0205194 |
| VN vs VG | -0.1812298 | -0.4233280 | 0.0608685 | 0.1423248 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.076
## Unadjusted ICC: 0.069
i = i+12.2.15 aTG
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aTG"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.87910 0.09486 9.267 < 2e-16 ***
## SEXM 0.36145 0.07946 4.549 1.08e-05 ***
## GRPVG -0.10188 0.12619 -0.807 0.421
## GRPVN -0.14265 0.10913 -1.307 0.193
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.505 1.82 0.410 0.6353
## s(FAM) 25.215 92.00 0.382 0.0333 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.238 Deviance explained = 36%
## GCV = 0.3243 Scale est. = 0.27102 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 20.69 1.08e-05
## GRP 2 0.86 0.425
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.505 1.820 0.410 0.6353
## s(FAM) 25.215 92.000 0.382 0.0333
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformation of the outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.38473 0.11244 -3.422 0.000801 ***
## SEXM 0.46812 0.09142 5.121 9.15e-07 ***
## GRPVG -0.10587 0.15059 -0.703 0.483127
## GRPVN -0.14858 0.13007 -1.142 0.255124
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.966 2.424 1.177 0.2659
## s(FAM) 29.956 92.000 0.488 0.0131 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.297 Deviance explained = 42.9%
## GCV = 0.44134 Scale est. = 0.35656 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 26.221 9.15e-07
## GRP 2 0.657 0.52
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.966 2.424 1.177 0.2659
## s(FAM) 29.956 92.000 0.488 0.0131
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Looks better after log-transformation
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) -0.79843363 0.336645341 -2.3717353
## GRPVG -0.10216949 0.129924623 -0.7863751
## GRPVN -0.12416055 0.112627926 -1.1023958
## SEXM 0.43015139 0.095466853 4.5057669
## aAGE 0.01035487 0.009102034 1.1376431
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on log2(Triglyceride), estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.1241606 | -0.3449072 | 0.0965861 | 0.2702896 |
| VG vs OM | -0.1021695 | -0.3568171 | 0.1524781 | 0.4316477 |
| VN vs VG | -0.0219911 | -0.2499414 | 0.2059593 | 0.8500272 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.199
## Unadjusted ICC: 0.175
i = i+12.2.16 aCa
Data selection
Open code
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aCa"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.426686 0.012353 196.450 < 2e-16 ***
## SEXM 0.051183 0.010275 4.981 1.68e-06 ***
## GRPVG 0.006650 0.016500 0.403 0.688
## GRPVN 0.005106 0.014229 0.359 0.720
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.558 3.166 1.358 0.2312
## s(FAM) 26.300 92.000 0.405 0.0269 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.255 Deviance explained = 38.3%
## GCV = 0.0054747 Scale est. = 0.0045128 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 24.812 1.68e-06
## GRP 2 0.095 0.91
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.558 3.166 1.358 0.2312
## s(FAM) 26.300 92.000 0.405 0.0269
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 2.462634671 0.040767673 60.4065546
## GRPVG 0.008100938 0.015733842 0.5148735
## GRPVN 0.006486519 0.013639216 0.4755786
## SEXM 0.052839772 0.011561014 4.5705137
## aAGE -0.001061304 0.001102254 -0.9628488
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on serum calcium level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.0064865 | -0.0202459 | 0.0332189 | 0.6343746 |
| VG vs OM | 0.0081009 | -0.0227368 | 0.0389387 | 0.6066414 |
| VN vs VG | -0.0016144 | -0.0292191 | 0.0259903 | 0.9087421 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.161
## Unadjusted ICC: 0.144
i = i+12.2.17 aP
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aP"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.193920 0.022760 52.457 < 2e-16 ***
## SEXM -0.146559 0.020403 -7.183 2.09e-11 ***
## GRPVG 0.001267 0.029857 0.042 0.966
## GRPVN -0.029071 0.025818 -1.126 0.262
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.766 2.208 0.859 0.411
## s(FAM) 12.635 92.000 0.159 0.203
##
## R-sq.(adj) = 0.279 Deviance explained = 34.7%
## GCV = 0.020032 Scale est. = 0.01806 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 51.597 2.09e-11
## GRP 2 0.944 0.391
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.766 2.208 0.859 0.411
## s(FAM) 12.635 92.000 0.159 0.203
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 1.277869210 0.072799038 17.55338056
## GRPVG -0.001110312 0.028096000 -0.03951849
## GRPVN -0.030992329 0.024355616 -1.27249212
## SEXM -0.148820190 0.020644560 -7.20868783
## aAGE -0.002218743 0.001968301 -1.12723764
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on serum phosphorous level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.0309923 | -0.0787285 | 0.0167438 | 0.2031983 |
| VG vs OM | -0.0011103 | -0.0561775 | 0.0539568 | 0.9684770 |
| VN vs VG | -0.0298820 | -0.0791759 | 0.0194119 | 0.2347813 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.046
## Unadjusted ICC: 0.035
i = i+12.2.18 aMg
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Mg))
column_name
## [1] "aMg"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Mg +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Mg + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.813448 0.011287 72.072 <2e-16 ***
## SEXM 0.009663 0.010202 0.947 0.345
## GRPVG -0.012224 0.013869 -0.881 0.379
## GRPVN -0.007972 0.012105 -0.659 0.511
## aSUP_Mg 0.004156 0.012763 0.326 0.745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000e+00 1 0.18 0.672
## s(FAM) 1.002e-06 92 0.00 0.572
##
## R-sq.(adj) = -0.014 Deviance explained = 1.33%
## GCV = 0.0047049 Scale est. = 0.004554 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Mg + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 0.897 0.345
## GRP 2 0.411 0.664
## aSUP_Mg 1 0.106 0.745
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000e+00 1.000e+00 0.18 0.672
## s(FAM) 1.002e-06 9.200e+01 0.00 0.572
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Mg')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 0.782640171 0.0332342104 23.5492332
## GRPVG -0.001784129 0.0128253368 -0.1391097
## GRPVN 0.002291817 0.0111942156 0.2047322
## SEXM 0.014742315 0.0094346150 1.5625773
## aAGE 0.000473197 0.0009047637 0.5230061
## aSUP_Mg 0.001214427 0.0118025611 0.1028952
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on serum magnesium level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.0022918 | -0.0196484 | 0.0242321 | 0.8377813 |
| VG vs OM | -0.0017841 | -0.0269213 | 0.0233531 | 0.8893635 |
| VN vs VG | 0.0040759 | -0.0185350 | 0.0266869 | 0.7238553 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Mg'))
## boundary (singular) fit: see help('isSingular')
res2 <- emm(mod_main)
## boundary (singular) fit: see help('isSingular')
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Mg'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Mg'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Mg'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without other
mod_other_cov <- rlme(type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Mg'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## Warning: Can't compute random effect variances. Some variance components equal
## zero. Your model may suffer from singularity (see `?lme4::isSingular`
## and `?performance::check_singularity`).
## Solution: Respecify random structure! You may also decrease the
## `tolerance` level to enforce the calculation of random effect variances.
## [1] NA
i = i+12.2.19 aSe
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Se))
column_name
## [1] "aSe"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Se +
s(aAGE) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Se + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.06223 0.06119 17.360 <2e-16 ***
## SEXM -0.03687 0.03269 -1.128 0.262
## GRPVG -0.08043 0.08635 -0.932 0.354
## GRPVN -0.06811 0.07409 -0.919 0.360
## aSUP_Se 0.23479 0.14231 1.650 0.102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 4.84 5.767 1.337 0.341
## s(FAM) 68.75 92.000 2.767 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.612 Deviance explained = 77.4%
## GCV = 0.070466 Scale est. = 0.040852 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Se + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 1.273 0.262
## GRP 2 0.552 0.577
## aSUP_Se 1 2.722 0.102
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 4.840 5.767 1.337 0.341
## s(FAM) 68.748 92.000 2.767 <2e-16
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_Se +
s(aAGE) +
s(FAM, bs='re'),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Se + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05564 0.09008 0.618 0.538
## SEXM -0.07567 0.04819 -1.570 0.119
## GRPVG -0.15359 0.12717 -1.208 0.230
## GRPVN -0.14668 0.10901 -1.345 0.181
## aSUP_Se 0.23895 0.20965 1.140 0.257
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 5.319 6.269 1.147 0.354
## s(FAM) 68.805 92.000 3.013 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.631 Deviance explained = 78.6%
## GCV = 0.15228 Scale est. = 0.087849 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Se + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 2.465 0.119
## GRP 2 1.061 0.350
## aSUP_Se 1 1.299 0.257
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 5.319 6.269 1.147 0.354
## s(FAM) 68.805 92.000 3.013 <2e-16
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
The distribution of residuals seem better. Lets continue continue on log-scale
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Se')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 0.0276922229 0.216358656 0.12799221
## GRPVG -0.1614851619 0.123469879 -1.30789114
## GRPVN -0.1095699976 0.106428421 -1.02951821
## SEXM -0.0458851521 0.038340344 -1.19678510
## aAGE 0.0004907061 0.005624277 0.08724785
## aSUP_Se 0.1771021236 0.169588671 1.04430398
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on serum selenium level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.1095700 | -0.3181659 | 0.0990259 | 0.3032362 |
| VG vs OM | -0.1614852 | -0.4034817 | 0.0805114 | 0.1909102 |
| VN vs VG | 0.0519152 | -0.1624849 | 0.2663152 | 0.6350801 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Se'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Se'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Se'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Se'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Se'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.610
## Unadjusted ICC: 0.591
i = i+12.2.20 aZn
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Zn))
column_name
## [1] "aZn"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Zn +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Zn + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.9444 0.4593 30.358 < 2e-16 ***
## SEXM 0.6261 0.2717 2.305 0.022943 *
## GRPVG -0.9372 0.6315 -1.484 0.140467
## GRPVN -1.8437 0.5444 -3.386 0.000963 ***
## aSUP_Zn 0.9758 0.6364 1.533 0.127885
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.583 3.152 1.105 0.34
## s(FAM) 61.448 92.000 2.120 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.566 Deviance explained = 72.4%
## GCV = 4.7176 Scale est. = 2.9761 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Zn + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 5.311 0.02294
## GRP 2 5.832 0.00384
## aSUP_Zn 1 2.351 0.12788
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.583 3.152 1.105 0.34
## s(FAM) 61.448 92.000 2.120 <2e-16
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
After log-transformation
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_Zn +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Zn + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.78394 0.04745 79.739 < 2e-16 ***
## SEXM 0.06440 0.02860 2.251 0.026190 *
## GRPVG -0.10029 0.06509 -1.541 0.125988
## GRPVN -0.20640 0.05615 -3.676 0.000356 ***
## aSUP_Zn 0.08062 0.06632 1.216 0.226566
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.348 2.864 0.997 0.336
## s(FAM) 60.118 92.000 1.977 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.554 Deviance explained = 71.4%
## GCV = 0.051838 Scale est. = 0.033136 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Zn + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 5.069 0.02619
## GRP 2 6.917 0.00144
## aSUP_Zn 1 1.477 0.22657
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.348 2.864 0.997 0.336
## s(FAM) 60.118 92.000 1.977 <2e-16
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
The distribution of residuals seem better. Lets continue continue on log-scale
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Zn')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 3.887343605 0.126230405 30.7956201
## GRPVG -0.131065316 0.056579630 -2.3164753
## GRPVN -0.220903409 0.048961313 -4.5117950
## SEXM 0.065841962 0.027535198 2.3911926
## aAGE -0.002671081 0.003399141 -0.7858105
## aSUP_Zn 0.065413966 0.060617216 1.0791318
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on serum log2(zinc) level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.2209034 | -0.3168658 | -0.1249410 | 0.0000064 |
| VG vs OM | -0.1310653 | -0.2419594 | -0.0201713 | 0.0205323 |
| VN vs VG | -0.0898381 | -0.1889434 | 0.0092672 | 0.0756192 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Zn'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Zn'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Zn'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Zn'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Zn'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.496
## Unadjusted ICC: 0.437
i = i+12.2.21 aFE
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aFE"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.6378 1.3117 14.209 < 2e-16 ***
## SEXM 3.6584 1.1347 3.224 0.00153 **
## GRPVG -1.3003 1.7111 -0.760 0.44840
## GRPVN 0.1878 1.4924 0.126 0.90002
## aSUP_Fe -1.7995 2.0216 -0.890 0.37474
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.261 0.6101
## s(FAM) 20.28 92 0.285 0.0787 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.161 Deviance explained = 27.5%
## GCV = 62.471 Scale est. = 53.693 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 10.396 0.00153
## GRP 2 0.497 0.60918
## aSUP_Fe 1 0.792 0.37474
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.261 0.6101
## s(FAM) 20.28 92.00 0.285 0.0787
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
log2 outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.13282 0.10672 38.727 < 2e-16 ***
## SEXM 0.27037 0.09238 2.927 0.00392 **
## GRPVG -0.22027 0.13927 -1.582 0.11570
## GRPVN -0.01026 0.12140 -0.085 0.93275
## aSUP_Fe -0.11400 0.16456 -0.693 0.48944
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.243 1.426 0.569 0.6077
## s(FAM) 20.150 92.000 0.282 0.0818 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.162 Deviance explained = 27.6%
## GCV = 0.41415 Scale est. = 0.3557 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 8.565 0.00392
## GRP 2 1.682 0.18918
## aSUP_Fe 1 0.480 0.48944
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.243 1.426 0.569 0.6077
## s(FAM) 20.150 92.000 0.282 0.0818
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation of the outcome did not substantially improve the homogeneity of variance and the normality of residuals. Lets continue with the original scale
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 18.18006142 4.0467742 4.4924823
## GRPVG -2.48750348 1.5549749 -1.5997065
## GRPVN 0.18154880 1.3582558 0.1336632
## SEXM 3.27037151 1.1600157 2.8192476
## aAGE 0.01293276 0.1101416 0.1174194
## aSUP_Fe -1.83953673 1.9284056 -0.9539159
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on serum Fe level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.1815488 | -2.4805837 | 2.8436813 | 0.8936689 |
| VG vs OM | -2.4875035 | -5.5351983 | 0.5601913 | 0.1096637 |
| VN vs VG | 2.6690523 | -0.0756487 | 5.4137532 | 0.0566575 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Fe'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Fe'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Fe'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Fe'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.116
## Unadjusted ICC: 0.108
i = i+12.2.22 aVKFE
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aVKFE"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 71.4903 1.7914 39.907 < 2e-16 ***
## SEXM -5.4486 1.3371 -4.075 7.72e-05 ***
## GRPVG 1.5470 2.4045 0.643 0.521
## GRPVN 2.8019 2.0919 1.339 0.183
## aSUP_Fe -0.2508 2.6042 -0.096 0.923
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.453 0.501944
## s(FAM) 42.67 92 0.878 0.000345 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.343 Deviance explained = 51.1%
## GCV = 97.568 Scale est. = 72.174 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 16.605 7.72e-05
## GRP 2 0.903 0.408
## aSUP_Fe 1 0.009 0.923
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.453 0.501944
## s(FAM) 42.67 92.00 0.878 0.000345
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
log2 outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.147430 0.033283 184.700 < 2e-16 ***
## SEXM -0.106389 0.026572 -4.004 9.85e-05 ***
## GRPVG 0.029599 0.044150 0.670 0.504
## GRPVN 0.049308 0.038455 1.282 0.202
## aSUP_Fe -0.005837 0.049833 -0.117 0.907
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.483 0.48803
## s(FAM) 33.62 92 0.583 0.00588 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.268 Deviance explained = 42%
## GCV = 0.036711 Scale est. = 0.028933 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 16.030 9.85e-05
## GRP 2 0.823 0.441
## aSUP_Fe 1 0.014 0.907
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.483 0.48803
## s(FAM) 33.62 92.00 0.583 0.00588
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation seemingly improved the homogeneity of variance
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 6.075582366 0.095644468 63.5225692
## GRPVG 0.036178181 0.036751432 0.9844019
## GRPVN 0.031635386 0.032102028 0.9854638
## SEXM -0.100683118 0.027416674 -3.6723316
## aAGE 0.001942899 0.002603168 0.7463596
## aSUP_Fe -0.032328395 0.045577371 -0.7093080
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on serum log2(ferritin Fe binding capacity), estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.0316354 | -0.0312834 | 0.0945542 | 0.3243963 |
| VG vs OM | 0.0361782 | -0.0358533 | 0.1082097 | 0.3249179 |
| VN vs VG | -0.0045428 | -0.0694131 | 0.0603275 | 0.8908299 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Fe'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Fe'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Fe'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Fe'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.220
## Unadjusted ICC: 0.203
i = i+12.2.23 aFERR
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aFERR"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.386 7.035 6.025 1.09e-08 ***
## SEXM 42.169 6.168 6.837 1.55e-10 ***
## GRPVG -24.171 9.159 -2.639 0.00912 **
## GRPVN -34.502 7.980 -4.323 2.67e-05 ***
## aSUP_Fe 14.878 10.893 1.366 0.17385
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.488 1.808 0.264 0.738
## s(FAM) 17.740 92.000 0.242 0.104
##
## R-sq.(adj) = 0.335 Deviance explained = 41.8%
## GCV = 1825.1 Scale est. = 1588.6 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 46.741 1.55e-10
## GRP 2 9.402 0.000137
## aSUP_Fe 1 1.866 0.173855
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.488 1.808 0.264 0.738
## s(FAM) 17.740 92.000 0.242 0.104
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
log2 outcome - better
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.7040 0.1692 27.803 < 2e-16 ***
## SEXM 1.3603 0.1308 10.402 < 2e-16 ***
## GRPVG -0.5936 0.2261 -2.625 0.0096 **
## GRPVN -0.8608 0.1965 -4.380 2.29e-05 ***
## aSUP_Fe 0.2563 0.2502 1.024 0.3075
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.744 2.122 0.824 0.50535
## s(FAM) 38.137 92.000 0.717 0.00159 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.548 Deviance explained = 65.5%
## GCV = 0.91313 Scale est. = 0.69397 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 108.196 < 2e-16
## GRP 2 9.631 0.00012
## aSUP_Fe 1 1.049 0.30747
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.744 2.122 0.824 0.50535
## s(FAM) 38.137 92.000 0.717 0.00159
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Much better after outcome transformation
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 3.98367629 0.48437215 8.2244123
## GRPVG -0.54507886 0.18612023 -2.9286385
## GRPVN -0.74401707 0.16257426 -4.5764753
## SEXM 1.37526662 0.13884622 9.9049624
## aAGE 0.01871669 0.01318322 1.4197363
## aSUP_Fe 0.05755952 0.23081742 0.2493725
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on serum log2(ferritin) level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.7440171 | -1.0626568 | -0.4253774 | 0.0000047 |
| VG vs OM | -0.5450789 | -0.9098678 | -0.1802899 | 0.0034045 |
| VN vs VG | -0.1989382 | -0.5274608 | 0.1295844 | 0.2352811 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Fe'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Fe'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Fe'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Fe'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.264
## Unadjusted ICC: 0.160
i = i+12.2.24 aTRF
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aTRF"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.837370 0.071306 39.792 < 2e-16 ***
## SEXM -0.214291 0.052904 -4.051 8.49e-05 ***
## GRPVG 0.058553 0.095800 0.611 0.542
## GRPVN 0.110023 0.083337 1.320 0.189
## aSUP_Fe -0.009488 0.103362 -0.092 0.927
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.0 1 0.499 0.48108
## s(FAM) 43.4 92 0.906 0.00026 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.348 Deviance explained = 51.8%
## GCV = 0.15334 Scale est. = 0.11284 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 16.407 8.49e-05
## GRP 2 0.881 0.417
## aSUP_Fe 1 0.008 0.927
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.0 1.0 0.499 0.48108
## s(FAM) 43.4 92.0 0.906 0.00026
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
log2 outcome - better
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.492311 0.033337 44.765 < 2e-16 ***
## SEXM -0.105276 0.026460 -3.979 0.000109 ***
## GRPVG 0.028190 0.044269 0.637 0.525257
## GRPVN 0.048499 0.038555 1.258 0.210415
## aSUP_Fe -0.005117 0.049795 -0.103 0.918288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.526 0.46934
## s(FAM) 34.47 92 0.607 0.00469 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.274 Deviance explained = 42.8%
## GCV = 0.036567 Scale est. = 0.028653 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 15.830 0.000109
## GRP 2 0.793 0.454369
## aSUP_Fe 1 0.011 0.918288
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.526 0.46934
## s(FAM) 34.47 92.00 0.607 0.00469
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Much better after the outcome transformation
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 1.419155345 0.095458606 14.8667093
## GRPVG 0.033604406 0.036680015 0.9161503
## GRPVN 0.029896148 0.032039645 0.9330986
## SEXM -0.099664752 0.027363396 -3.6422655
## aAGE 0.001990978 0.002598109 0.7663180
## aSUP_Fe -0.030074204 0.045488802 -0.6611342
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on serum log2(transferin) level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.0298961 | -0.0329004 | 0.0926927 | 0.3507691 |
| VG vs OM | 0.0336044 | -0.0382871 | 0.1054959 | 0.3595881 |
| VN vs VG | -0.0037083 | -0.0684525 | 0.0610360 | 0.9106189 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Fe'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Fe'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Fe'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Fe'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.227
## Unadjusted ICC: 0.210
i = i+12.2.25 aSATTRF
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aSATTRF"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.29766 2.05742 12.782 < 2e-16 ***
## SEXM 7.51310 1.72073 4.366 2.31e-05 ***
## GRPVG -2.18168 2.70830 -0.806 0.422
## GRPVN -0.01361 2.35702 -0.006 0.995
## aSUP_Fe -2.05056 3.13785 -0.653 0.514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.68 2.06 0.685 0.5277
## s(FAM) 26.28 92.00 0.402 0.0303 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.231 Deviance explained = 36.3%
## GCV = 148.46 Scale est. = 122.29 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 19.064 2.31e-05
## GRP 2 0.458 0.633
## aSUP_Fe 1 0.427 0.514
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.68 2.06 0.685 0.5277
## s(FAM) 26.28 92.00 0.402 0.0303
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
log2 outcome - not better
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.63208 0.11766 39.368 < 2e-16 ***
## SEXM 0.37999 0.09877 3.847 0.000174 ***
## GRPVG -0.25266 0.15475 -1.633 0.104553
## GRPVN -0.05901 0.13470 -0.438 0.661934
## aSUP_Fe -0.10160 0.17967 -0.565 0.572558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.631 1.994 0.753 0.4914
## s(FAM) 25.660 92.000 0.388 0.0346 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.219 Deviance explained = 35.1%
## GCV = 0.48747 Scale est. = 0.4033 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 14.801 0.000174
## GRP 2 1.479 0.231104
## aSUP_Fe 1 0.320 0.572558
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.631 1.994 0.753 0.4914
## s(FAM) 25.660 92.000 0.388 0.0346
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
The outcome transformation did not help much
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 26.87526984 6.2058984 4.33060097
## GRPVG -4.19740872 2.3846194 -1.76020067
## GRPVN -0.36110427 2.0829425 -0.17336258
## SEXM 6.93260884 1.7789329 3.89706032
## aAGE -0.01345092 0.1689067 -0.07963522
## aSUP_Fe -2.62076236 2.9572911 -0.88620372
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on transferin saturation, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.3611043 | -4.4435965 | 3.7213879 | 0.8623664 |
| VG vs OM | -4.1974087 | -8.8711769 | 0.4763595 | 0.0783738 |
| VN vs VG | 3.8363044 | -0.3728099 | 8.0454188 | 0.0740400 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Fe'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Fe'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Fe'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Fe'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.146
## Unadjusted ICC: 0.132
i = i+12.2.26 aTRFINDEX
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aTRFINDEX"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.95116 0.05881 16.173 < 2e-16 ***
## SEXM -0.33494 0.05242 -6.389 1.66e-09 ***
## GRPVG 0.18595 0.07666 2.426 0.01637 *
## GRPVN 0.18512 0.06661 2.779 0.00609 **
## aSUP_Fe -0.13586 0.09143 -1.486 0.13922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.467 1.782 0.173 0.772
## s(FAM) 15.006 92.000 0.197 0.148
##
## R-sq.(adj) = 0.278 Deviance explained = 35.8%
## GCV = 0.12906 Scale est. = 0.11408 n = 185
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 40.824 1.66e-09
## GRP 2 4.464 0.013
## aSUP_Fe 1 2.208 0.139
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.467 1.782 0.173 0.772
## s(FAM) 15.006 92.000 0.197 0.148
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
log2 outcome - better
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.19498 0.07782 -2.506 0.01330 *
## SEXM -0.47732 0.06453 -7.397 9.39e-12 ***
## GRPVG 0.30103 0.10350 2.908 0.00419 **
## GRPVN 0.27629 0.08944 3.089 0.00240 **
## aSUP_Fe -0.21513 0.11849 -1.816 0.07144 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 3.01 3.706 1.377 0.2956
## s(FAM) 28.02 92.000 0.447 0.0182 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.397 Deviance explained = 51.2%
## GCV = 0.20984 Scale est. = 0.16896 n = 185
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 54.717 9.39e-12
## GRP 2 5.858 0.00356
## aSUP_Fe 1 3.296 0.07144
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 3.010 3.706 1.377 0.2956
## s(FAM) 28.024 92.000 0.447 0.0182
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Better with log-transformation
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) -0.097216042 0.248556260 -0.3911229
## GRPVG 0.298259746 0.095977711 3.1075939
## GRPVN 0.246885648 0.083529803 2.9556594
## SEXM -0.444614135 0.071479584 -6.2201557
## aAGE -0.003277372 0.006761608 -0.4847031
## aSUP_Fe -0.121580832 0.118393539 -1.0269212
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on log2(sTfR index - ratio), estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.2468856 | 0.0831702 | 0.4106011 | 0.0031200 |
| VG vs OM | 0.2982597 | 0.1101469 | 0.4863726 | 0.0018862 |
| VN vs VG | -0.0513741 | -0.2214201 | 0.1186719 | 0.5537556 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Fe'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Fe'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Fe'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Fe'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.197
## Unadjusted ICC: 0.147
i = i+12.2.27 aSTRF
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Fe))
column_name
## [1] "aSTRF"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1336 0.2032 5.580 8.89e-08 ***
## SEXM 0.1141 0.1910 0.597 0.551
## GRPVG 0.1040 0.2609 0.399 0.691
## GRPVN 0.2433 0.2265 1.074 0.284
## aSUP_Fe -0.2486 0.3199 -0.777 0.438
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000 1 0.121 0.728
## s(FAM) 2.855 92 0.032 0.424
##
## R-sq.(adj) = 0.00105 Deviance explained = 4.35%
## GCV = 1.619 Scale est. = 1.5419 n = 186
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 0.357 0.551
## GRP 2 0.601 0.550
## aSUP_Fe 1 0.604 0.438
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000 1.000 0.121 0.728
## s(FAM) 2.855 92.000 0.032 0.424
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
log2 outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_Fe +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.231691 0.066562 3.481 0.000631 ***
## SEXM -0.001934 0.061939 -0.031 0.975122
## GRPVG 0.105664 0.085709 1.233 0.219295
## GRPVN 0.087213 0.074413 1.172 0.242789
## aSUP_Fe -0.195472 0.104562 -1.869 0.063236 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.0 1 0.232 0.631
## s(FAM) 5.3 92 0.061 0.367
##
## R-sq.(adj) = 0.0301 Deviance explained = 8.41%
## GCV = 0.17222 Scale est. = 0.16175 n = 186
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Fe + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 0.001 0.9751
## GRP 2 0.940 0.3927
## aSUP_Fe 1 3.495 0.0632
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.0 1.0 0.232 0.631
## s(FAM) 5.3 92.0 0.061 0.367
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Seems better with log-transformation
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 0.175287099 0.160494719 1.0921674
## GRPVG 0.098721009 0.061995489 1.5923902
## GRPVN 0.038502452 0.053833496 0.7152137
## SEXM -0.021711556 0.046076149 -0.4712103
## aAGE 0.001874805 0.004366848 0.4293269
## aSUP_Fe -0.160991370 0.076451202 -2.1058056
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on log2(soluble transferon receptor), estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.0385025 | -0.0670093 | 0.1440142 | 0.4744770 |
| VG vs OM | 0.0987210 | -0.0227879 | 0.2202299 | 0.1112971 |
| VN vs VG | -0.0602186 | -0.1698312 | 0.0493941 | 0.2815889 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Fe'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Fe'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Fe'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Fe'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Fe'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.014
## Unadjusted ICC: 0.013
i = i+12.2.28 aHGB
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aHGB"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 136.931 2.067 66.26 <2e-16 ***
## SEXM 17.346 1.759 9.86 <2e-16 ***
## GRPVG -1.313 2.737 -0.48 0.632
## GRPVN -2.394 2.370 -1.01 0.314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.074 1.135 1.520 0.2400
## s(FAM) 22.439 92.000 0.325 0.0569 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.406 Deviance explained = 49.1%
## GCV = 156.38 Scale est. = 133.37 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 97.211 <2e-16
## GRP 2 0.514 0.599
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.074 1.135 1.520 0.2400
## s(FAM) 22.439 92.000 0.325 0.0569
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 142.4861828 5.114829 27.8574701
## GRPVG -1.1030400 1.974013 -0.5587806
## GRPVN -1.0930815 1.711215 -0.6387751
## SEXM 18.1167604 1.450478 12.4902021
## aAGE -0.1694821 0.138292 -1.2255383
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on hemoglobin level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -1.0930815 | -4.447001 | 2.260838 | 0.5229692 |
| VG vs OM | -1.1030400 | -4.972034 | 2.765954 | 0.5763114 |
| VN vs VG | 0.0099585 | -3.453410 | 3.473327 | 0.9955034 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.131
## Unadjusted ICC: 0.088
i = i+12.2.29 aMCV
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aMCV"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 89.1372 0.6604 134.968 < 2e-16 ***
## SEXM -1.6668 0.6198 -2.689 0.00783 **
## GRPVG -0.9380 0.8546 -1.098 0.27388
## GRPVN 1.0481 0.7408 1.415 0.15883
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000 1 1.037 0.310
## s(FAM) 2.481 92 0.028 0.432
##
## R-sq.(adj) = 0.0627 Deviance explained = 9.53%
## GCV = 17.521 Scale est. = 16.82 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 7.233 0.00783
## GRP 2 3.509 0.03200
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000 1.000 1.037 0.310
## s(FAM) 2.481 92.000 0.028 0.432
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 86.51614413 1.84167801 46.976802
## GRPVG -0.81659870 0.71077568 -1.148884
## GRPVN 1.65981932 0.61615104 2.693851
## SEXM -1.17976077 0.52226834 -2.258917
## aAGE 0.05637678 0.04979429 1.132194
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on mean corpuscular volume, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 1.6598193 | 0.4521855 | 2.867453 | 0.0070632 |
| VG vs OM | -0.8165987 | -2.2096934 | 0.576496 | 0.2506039 |
| VN vs VG | 2.4764180 | 1.2293754 | 3.723461 | 0.0000994 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.000
## Unadjusted ICC: 0.000
i = i+12.2.30 aPTH
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aPTH"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1794 0.1926 16.508 <2e-16 ***
## SEXM -0.1709 0.1596 -1.071 0.2860
## GRPVG -0.1681 0.2564 -0.656 0.5130
## GRPVN 0.4819 0.2220 2.171 0.0315 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 1.244 0.2664
## s(FAM) 26.99 92 0.418 0.0268 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.209 Deviance explained = 34.1%
## GCV = 1.3187 Scale est. = 1.0931 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 1.146 0.28603
## GRP 2 4.803 0.00946
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 1.244 0.2664
## s(FAM) 26.99 92.00 0.418 0.0268
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
log2 outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.567467 0.086576 18.105 <2e-16 ***
## SEXM -0.005991 0.067575 -0.089 0.9295
## GRPVG -0.086925 0.116526 -0.746 0.4569
## GRPVN 0.175315 0.100814 1.739 0.0841 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.915 0.34047
## s(FAM) 35.99 92 0.647 0.00334 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.269 Deviance explained = 42.6%
## GCV = 0.24834 Scale est. = 0.19391 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 0.008 0.929
## GRP 2 3.591 0.030
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.915 0.34047
## s(FAM) 35.99 92.00 0.647 0.00334
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Not substantially better
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 2.34084694 0.56786353 4.1221998
## GRPVG -0.15111251 0.21916078 -0.6895052
## GRPVN 0.36768227 0.18998419 1.9353309
## SEXM -0.03930107 0.16103637 -0.2440509
## aAGE 0.02080269 0.01535359 1.3549079
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on parathormone level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.3676823 | -0.0046799 | 0.7400444 | 0.0529497 |
| VG vs OM | -0.1511125 | -0.5806597 | 0.2784347 | 0.4905054 |
| VN vs VG | 0.5187948 | 0.1342813 | 0.9033082 | 0.0081829 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.164
## Unadjusted ICC: 0.154
i = i+12.2.31 aCros
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Ca),
!is.na(aSUP_D))
column_name
## [1] "aCros"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Ca +
aSUP_D +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Ca + aSUP_D + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.425831 0.038721 10.997 <2e-16 ***
## SEXM -0.011581 0.034635 -0.334 0.738
## GRPVG -0.045738 0.049547 -0.923 0.357
## GRPVN -0.000450 0.043210 -0.010 0.992
## aSUP_Ca 0.009751 0.068515 0.142 0.887
## aSUP_D 0.048733 0.037304 1.306 0.193
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.291e+00 2.929 5.898 0.00112 **
## s(FAM) 1.178e-07 92.000 0.000 0.54873
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0879 Deviance explained = 12.4%
## GCV = 0.054416 Scale est. = 0.052004 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Ca + aSUP_D + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 0.112 0.738
## GRP 2 0.645 0.526
## aSUP_Ca 1 0.020 0.887
## aSUP_D 1 1.707 0.193
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.291e+00 2.929e+00 5.898 0.00112
## s(FAM) 1.178e-07 9.200e+01 0.000 0.54873
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
log2 outcome - better
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_Ca +
aSUP_D +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Ca + aSUP_D + s(aAGE) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.4809125 0.1308232 -11.320 <2e-16 ***
## SEXM 0.0691562 0.1161445 0.595 0.552
## GRPVG -0.1307583 0.1675505 -0.780 0.436
## GRPVN 0.0002462 0.1462614 0.002 0.999
## aSUP_Ca 0.0206799 0.2307594 0.090 0.929
## aSUP_D 0.1429459 0.1261068 1.134 0.259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.951 2.487 5.742 0.0023 **
## s(FAM) 1.741 92.000 0.019 0.4349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0736 Deviance explained = 11.7%
## GCV = 0.61672 Scale est. = 0.58476 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Ca + aSUP_D + s(aAGE) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 0.355 0.552
## GRP 2 0.469 0.627
## aSUP_Ca 1 0.008 0.929
## aSUP_D 1 1.285 0.259
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.951 2.487 5.742 0.0023
## s(FAM) 1.741 92.000 0.019 0.4349
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Seems better with log-transformation
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Ca', 'aSUP_D')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) -0.27779683 0.40465400 -0.6865046
## GRPVG -0.10107124 0.16246134 -0.6221249
## GRPVN 0.05109413 0.14220856 0.3592901
## SEXM 0.12850192 0.11385367 1.1286586
## aAGE -0.03412457 0.01086923 -3.1395570
## aSUP_Ca 0.18519948 0.22425288 0.8258510
## aSUP_D 0.08029725 0.12283772 0.6536856
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on log2(beta-CrossLaps) level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.0510941 | -0.2276295 | 0.3298178 | 0.7193781 |
| VG vs OM | -0.1010712 | -0.4194896 | 0.2173471 | 0.5338598 |
| VN vs VG | 0.1521654 | -0.1200353 | 0.4243661 | 0.2732289 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Ca', 'aSUP_D'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Ca', 'aSUP_D'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Ca', 'aSUP_D'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Ca', 'aSUP_D'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Ca', 'aSUP_D'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.012
## Unadjusted ICC: 0.011
i = i+12.2.32 aP1NP
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_Ca),
!is.na(aSUP_D))
column_name
## [1] "aP1NP"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Ca +
aSUP_D +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Ca + aSUP_D + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.2496 4.0177 12.009 <2e-16 ***
## SEXM -3.7322 3.5904 -1.039 0.3000
## GRPVG 0.7933 5.1232 0.155 0.8771
## GRPVN 6.9323 4.4846 1.546 0.1239
## aSUP_Ca -5.3566 7.0718 -0.757 0.4498
## aSUP_D 7.9271 3.8737 2.046 0.0422 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000e+00 1 6.934 0.00919 **
## s(FAM) 2.443e-09 94 0.000 0.67144
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0832 Deviance explained = 11.3%
## GCV = 583.01 Scale est. = 561.19 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Ca + aSUP_D + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 1.081 0.3000
## GRP 2 1.638 0.1972
## aSUP_Ca 1 0.574 0.4498
## aSUP_D 1 4.188 0.0422
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000e+00 1.000e+00 6.934 0.00919
## s(FAM) 2.443e-09 9.400e+01 0.000 0.67144
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
log2 outcome - better
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_Ca +
aSUP_D +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Ca + aSUP_D + s(aAGE) + s(FAM,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.459829 0.096778 56.416 <2e-16 ***
## SEXM -0.001644 0.086485 -0.019 0.985
## GRPVG 0.024800 0.123409 0.201 0.841
## GRPVN 0.153014 0.108024 1.416 0.158
## aSUP_Ca -0.115319 0.170347 -0.677 0.499
## aSUP_D 0.178770 0.093310 1.916 0.057 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000e+00 1 8.353 0.00432 **
## s(FAM) 1.416e-08 92 0.000 0.57808
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0714 Deviance explained = 10.1%
## GCV = 0.33828 Scale est. = 0.32562 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Ca + aSUP_D + s(aAGE) + s(FAM,
## bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 0.000 0.985
## GRP 2 1.315 0.271
## aSUP_Ca 1 0.458 0.499
## aSUP_D 1 3.671 0.057
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000e+00 1.000e+00 8.353 0.00432
## s(FAM) 1.416e-08 9.200e+01 0.000 0.57808
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Seems better after log-transformation
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Ca', 'aSUP_D')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 6.302159016 0.330844280 19.04871685
## GRPVG 0.008317194 0.132828059 0.06261624
## GRPVN 0.136865071 0.116269427 1.17713723
## SEXM 0.028827929 0.093086525 0.30968960
## aAGE -0.024181575 0.008886661 -2.72110910
## aSUP_Ca -0.101822423 0.183348697 -0.55534850
## aSUP_D 0.177087095 0.100431868 1.76325600
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on log2(procollagen type I) level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.1368651 | -0.0910188 | 0.3647490 | 0.2391407 |
| VG vs OM | 0.0083172 | -0.2520210 | 0.2686554 | 0.9500721 |
| VN vs VG | 0.1285479 | -0.0940029 | 0.3510986 | 0.2575933 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Ca', 'aSUP_D'))
## boundary (singular) fit: see help('isSingular')
res2 <- emm(mod_main)
## boundary (singular) fit: see help('isSingular')
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Ca', 'aSUP_D'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Ca', 'aSUP_D'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Ca', 'aSUP_D'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without other
mod_other_cov <- rlme(type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Ca', 'aSUP_D'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## Warning: Can't compute random effect variances. Some variance components equal
## zero. Your model may suffer from singularity (see `?lme4::isSingular`
## and `?performance::check_singularity`).
## Solution: Respecify random structure! You may also decrease the
## `tolerance` level to enforce the calculation of random effect variances.
## [1] NA
i = i+12.2.33 aUI
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aUI"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 137.9724 22.6542 6.090 1.43e-08 ***
## SEXM 0.8798 15.4427 0.057 0.955
## GRPVG -29.4705 30.8797 -0.954 0.342
## GRPVN -3.5951 26.4988 -0.136 0.892
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 3.554 0.0618 .
## s(FAM) 45.27 84 1.211 2.58e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.383 Deviance explained = 56.4%
## GCV = 12535 Scale est. = 8806.9 n = 169
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 0.003 0.955
## GRP 2 0.577 0.563
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 3.554 0.0618
## s(FAM) 45.27 84.00 1.211 2.58e-05
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again but with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.9473 0.3352 20.727 <2e-16 ***
## SEXM -0.1768 0.1556 -1.136 0.259
## GRPVG -0.7849 0.4745 -1.654 0.101
## GRPVN -0.5123 0.4030 -1.271 0.207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 4.732 5.661 1.221 0.3
## s(FAM) 67.786 84.000 3.969 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.683 Deviance explained = 82.5%
## GCV = 1.4734 Scale est. = 0.80628 n = 169
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 1.290 0.259
## GRP 2 1.453 0.239
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 4.732 5.661 1.221 0.3
## s(FAM) 67.786 84.000 3.969 <2e-16
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Transformation did substantially improve the residuals distribution
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0('log2_', column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 5.87765312 0.72041473 8.1587076
## GRPVG -0.51177760 0.34210167 -1.4959810
## GRPVN -0.41039200 0.29232831 -1.4038736
## SEXM -0.09179391 0.14190622 -0.6468632
## aAGE 0.02899618 0.01910877 1.5174274
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on urinary iodine level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.4103920 | -0.9833450 | 0.1625610 | 0.1603565 |
| VG vs OM | -0.5117776 | -1.1822846 | 0.1587294 | 0.1346586 |
| VN vs VG | 0.1013856 | -0.4838616 | 0.6866328 | 0.7342065 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.653
## Unadjusted ICC: 0.622
i = i+12.2.34 aUREA
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aUREA"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.38411 0.20867 21.010 < 2e-16 ***
## SEXM 0.66433 0.18762 3.541 0.000522 ***
## GRPVG 0.09245 0.27878 0.332 0.740603
## GRPVN -0.73765 0.23468 -3.143 0.001990 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 7.467 8.333 3.601 0.00101 **
## s(FAM) 14.727 92.000 0.193 0.14311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.319 Deviance explained = 41.1%
## GCV = 1.6659 Scale est. = 1.4325 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 12.538 0.000522
## GRP 2 7.846 0.000561
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 7.467 8.333 3.601 0.00101
## s(FAM) 14.727 92.000 0.193 0.14311
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again but with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.09327 0.06268 33.394 < 2e-16 ***
## SEXM 0.23203 0.05352 4.335 2.61e-05 ***
## GRPVG -0.02983 0.08407 -0.355 0.723235
## GRPVN -0.27819 0.07159 -3.886 0.000151 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 5.341 6.378 1.604 0.1320
## s(FAM) 22.633 92.000 0.331 0.0484 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.335 Deviance explained = 44.6%
## GCV = 0.14441 Scale est. = 0.11972 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 18.795 2.61e-05
## GRP 2 9.733 0.000104
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 5.341 6.378 1.604 0.1320
## s(FAM) 22.633 92.000 0.331 0.0484
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 1.869322448 0.204511767 9.140415
## GRPVG -0.099980430 0.078929101 -1.266712
## GRPVN -0.293105027 0.068421372 -4.283823
## SEXM 0.221267918 0.057996034 3.815225
## aAGE 0.007131657 0.005529478 1.289752
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on log2(urea) level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.2931050 | -0.4272085 | -0.1590016 | 0.0000184 |
| VG vs OM | -0.0999804 | -0.2546786 | 0.0547178 | 0.2052583 |
| VN vs VG | -0.1931246 | -0.3316042 | -0.0546450 | 0.0062688 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.141
## Unadjusted ICC: 0.112
i = i+12.2.35 aCREA
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aCREA"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.7561 1.8846 33.299 < 2e-16 ***
## SEXM 15.1981 1.5792 9.624 < 2e-16 ***
## GRPVG -0.9574 2.5548 -0.375 0.70839
## GRPVN -5.9688 2.1520 -2.774 0.00626 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 7.868 8.537 3.784 0.000436 ***
## s(FAM) 27.785 92.000 0.433 0.024844 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.53 Deviance explained = 62.8%
## GCV = 125.65 Scale est. = 99.004 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 92.623 <2e-16
## GRP 2 4.745 0.0101
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 7.868 8.537 3.784 0.000436
## s(FAM) 27.785 92.000 0.433 0.024844
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again but with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.94520 0.03825 155.411 < 2e-16 ***
## SEXM 0.33174 0.03098 10.709 < 2e-16 ***
## GRPVG -0.03510 0.05211 -0.674 0.50172
## GRPVN -0.12775 0.04398 -2.904 0.00427 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 7.683 8.399 2.994 0.00320 **
## s(FAM) 32.914 92.000 0.556 0.00872 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.563 Deviance explained = 66.6%
## GCV = 0.049779 Scale est. = 0.037908 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 114.673 < 2e-16
## GRP 2 4.794 0.00966
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 7.683 8.399 2.994 0.00320
## s(FAM) 32.914 92.000 0.556 0.00872
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 5.844416510 0.119777686 48.7938671
## GRPVG -0.073863948 0.046226901 -1.5978564
## GRPVN -0.132356761 0.040072773 -3.3029099
## SEXM 0.332572418 0.033966900 9.7910735
## aAGE 0.002980323 0.003238484 0.9202833
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on log2(creatinine) level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.1323568 | -0.210898 | -0.0538156 | 0.0009569 |
| VG vs OM | -0.0738639 | -0.164467 | 0.0167391 | 0.1100749 |
| VN vs VG | -0.0584928 | -0.139597 | 0.0226114 | 0.1574980 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.133
## Unadjusted ICC: 0.080
i = i+12.2.36 aUA
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))
column_name
## [1] "aUA"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 262.773 9.237 28.448 <2e-16 ***
## SEXM 89.682 8.112 11.056 <2e-16 ***
## GRPVG -5.856 12.150 -0.482 0.630
## GRPVN -7.832 10.525 -0.744 0.458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.0 1 4.897 0.0283 *
## s(FAM) 16.7 92 0.223 0.1258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.431 Deviance explained = 49.4%
## GCV = 3224.7 Scale est. = 2850.5 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 122.237 <2e-16
## GRP 2 0.281 0.756
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.0 1.0 4.897 0.0283
## s(FAM) 16.7 92.0 0.223 0.1258
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 336.068816 31.0908339 10.8092571
## GRPVG -8.029163 11.9991705 -0.6691431
## GRPVN -5.573346 10.4017366 -0.5358092
## SEXM 91.199419 8.8168280 10.3437902
## aAGE -2.117551 0.8406171 -2.5190432
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on uric acid level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -5.573346 | -25.96037 | 14.81368 | 0.5920905 |
| VG vs OM | -8.029163 | -31.54710 | 15.48878 | 0.5034042 |
| VN vs VG | 2.455817 | -18.59650 | 23.50814 | 0.8191520 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.091
## Unadjusted ICC: 0.057
i = i+12.2.37 aVIT_AKTB12
Data selection
Open code
## data selection
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSup_B12))
column_name
## [1] "aVIT_AKTB12"
gamm <- gam(
outcome ~
SEX +
GRP +
aSup_B12 +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSup_B12 + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 106.955 10.662 10.032 <2e-16 ***
## SEXM -21.083 8.720 -2.418 0.0168 *
## GRPVG -26.469 15.606 -1.696 0.0919 .
## GRPVN -7.692 16.161 -0.476 0.6348
## aSup_B12 22.701 13.170 1.724 0.0868 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 1.524 0.2189
## s(FAM) 28.77 92 0.459 0.0187 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.226 Deviance explained = 36.6%
## GCV = 3999.1 Scale est. = 3255.4 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSup_B12 + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 5.845 0.0168
## GRP 2 1.774 0.1732
## aSup_B12 1 2.971 0.0868
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 1.524 0.2189
## s(FAM) 28.77 92.00 0.459 0.0187
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again but with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSup_B12 +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSup_B12 + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.60198 0.11983 55.094 < 2e-16 ***
## SEXM -0.25397 0.08038 -3.159 0.00197 **
## GRPVG -0.36448 0.17827 -2.044 0.04295 *
## GRPVN -0.14558 0.18137 -0.803 0.42367
## aSup_B12 0.22160 0.14090 1.573 0.11823
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.0 1 0.023 0.879
## s(FAM) 52.7 92 1.355 4.34e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.444 Deviance explained = 61.7%
## GCV = 0.38929 Scale est. = 0.2671 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSup_B12 + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 9.982 0.00197
## GRP 2 2.302 0.10421
## aSup_B12 1 2.474 0.11823
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.0 1.0 0.023 0.879
## s(FAM) 52.7 92.0 1.355 4.34e-06
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the fit. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSup_B12')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 6.5532171286 0.340046293 19.27154409
## GRPVG -0.2998320029 0.165966435 -1.80658217
## GRPVN -0.0870977097 0.168498428 -0.51690518
## SEXM -0.2300316762 0.073237971 -3.14087999
## aAGE -0.0001034442 0.009109077 -0.01135617
## aSup_B12 0.1963498001 0.130122583 1.50896021
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on log2(vitamin B12) level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.0870977 | -0.4173486 | 0.2431531 | 0.6052224 |
| VG vs OM | -0.2998320 | -0.6251202 | 0.0254562 | 0.0708275 |
| VN vs VG | 0.2127343 | -0.0667704 | 0.4922390 | 0.1357643 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSup_B12'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSup_B12'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSup_B12'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSup_B12'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSup_B12'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.400
## Unadjusted ICC: 0.369
i = i+12.2.38 aHCY
Data selection
Open code
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSup_B12))
column_name
## [1] "aHCY"
gamm <- gam(
outcome ~
SEX +
GRP +
aSup_B12 +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSup_B12 + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.71028 1.19478 11.475 < 2e-16 ***
## SEXM 6.95489 1.10957 6.268 4.1e-09 ***
## GRPVG -0.05966 1.83444 -0.033 0.974
## GRPVN -1.42550 1.88299 -0.757 0.450
## aSup_B12 -0.28000 1.59650 -0.175 0.861
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.345 1.593 0.945 0.501
## s(FAM) 14.323 82.000 0.215 0.138
##
## R-sq.(adj) = 0.254 Deviance explained = 34.5%
## GCV = 53.027 Scale est. = 46.304 n = 163
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSup_B12 + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 39.289 4.1e-09
## GRP 2 0.439 0.646
## aSup_B12 1 0.031 0.861
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.345 1.593 0.945 0.501
## s(FAM) 14.323 82.000 0.215 0.138
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
log2 outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSup_B12 +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSup_B12 + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.685806 0.073061 50.448 < 2e-16 ***
## SEXM 0.552059 0.062247 8.869 5.3e-15 ***
## GRPVG 0.043697 0.113526 0.385 0.701
## GRPVN -0.095224 0.115761 -0.823 0.412
## aSup_B12 0.005408 0.096473 0.056 0.955
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.921 2.36 1.125 0.3336
## s(FAM) 27.472 82.00 0.520 0.0123 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.448 Deviance explained = 56.2%
## GCV = 0.18113 Scale est. = 0.14291 n = 163
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSup_B12 + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 78.656 5.3e-15
## GRP 2 0.966 0.383
## aSup_B12 1 0.003 0.955
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.921 2.360 1.125 0.3336
## s(FAM) 27.472 82.000 0.520 0.0123
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation slightly improved the fit. As log2 transformation was used in children category, we will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
include = c('aSup_B12'),
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 3.524246411 0.200539051 17.57386602
## GRPVG 0.105167460 0.088104632 1.19366551
## GRPVN -0.042892250 0.090826370 -0.47224446
## SEXM 0.481593485 0.058124036 8.28561679
## aAGE 0.003485828 0.005441813 0.64056374
## aSup_B12 0.002687899 0.078039153 0.03444295
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on log2(homocystain), estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.0428923 | -0.2209087 | 0.1351242 | 0.6367523 |
| VG vs OM | 0.1051675 | -0.0675144 | 0.2778494 | 0.2326088 |
| VN vs VG | -0.1480597 | -0.3006219 | 0.0045024 | 0.0571553 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
include = c('aSup_B12'),
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
include = c('aSup_B12'),
type = 'adult')
### model without sex
mod_nsex <- rlme(
exclude = c('SEX'),
include = c('aSup_B12'),
type = 'adult')
## boundary (singular) fit: see help('isSingular')
### model without diet groups
mod_ndiet <- rlme(
exclude = c('GRP'),
include = c('aSup_B12'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(
exclude = c('aAGE'),
include = c('aSup_B12'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## boundary (singular) fit: see help('isSingular')
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.213
## Unadjusted ICC: 0.148
i = i+12.2.39 aMMA
Data selection
Open code
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSup_B12))
column_name
## [1] "aMMA"
gamm <- gam(
outcome ~
SEX +
GRP +
aSup_B12 +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSup_B12 + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 165.03 71.01 2.324 0.0212 *
## SEXM 69.52 67.29 1.033 0.3029
## GRPVG 163.59 101.37 1.614 0.1083
## GRPVN 256.32 106.04 2.417 0.0166 *
## aSup_B12 -227.21 88.76 -2.560 0.0113 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000e+00 1 1.779 0.184
## s(FAM) 4.556e-09 93 0.000 0.590
##
## R-sq.(adj) = 0.035 Deviance explained = 6.1%
## GCV = 2.0516e+05 Scale est. = 1.9858e+05 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSup_B12 + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 1.068 0.3029
## GRP 2 2.922 0.0564
## aSup_B12 1 6.553 0.0113
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.000e+00 1.000e+00 1.779 0.184
## s(FAM) 4.556e-09 9.300e+01 0.000 0.590
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
log2 outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSup_B12 +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSup_B12 + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.52010 0.10523 71.463 < 2e-16 ***
## SEXM 0.17721 0.08985 1.972 0.050298 .
## GRPVG 0.46768 0.15317 3.053 0.002654 **
## GRPVN 0.30704 0.15908 1.930 0.055375 .
## aSup_B12 -0.45949 0.13077 -3.514 0.000575 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.112 1.203 2.450 0.1332
## s(FAM) 21.627 92.000 0.307 0.0717 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.239 Deviance explained = 34.8%
## GCV = 0.40864 Scale est. = 0.34802 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSup_B12 + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 3.890 0.050298
## GRP 2 4.664 0.010754
## aSup_B12 1 12.347 0.000575
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.112 1.203 2.450 0.1332
## s(FAM) 21.627 92.000 0.307 0.0717
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation slightly improved the fit. As log2 transformation was used in children category, we will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
include = c('aSup_B12'),
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 6.95490719 0.261011596 26.645970
## GRPVG 0.40509058 0.110962983 3.650682
## GRPVN 0.14131703 0.116073344 1.217480
## SEXM 0.09687987 0.073651193 1.315388
## aAGE 0.01641409 0.007052435 2.327435
## aSup_B12 -0.33754307 0.097155351 -3.474261
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on log2(methylmalonic acid) level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.1413170 | -0.0861825 | 0.3688166 | 0.2234215 |
| VG vs OM | 0.4050906 | 0.1876071 | 0.6225740 | 0.0002615 |
| VN vs VG | -0.2637736 | -0.4488089 | -0.0787382 | 0.0052062 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
include = c('aSup_B12'),
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
include = c('aSup_B12'),
type = 'adult')
### model without sex
mod_nsex <- rlme(
exclude = c('SEX'),
include = c('aSup_B12'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(
exclude = c('GRP'),
include = c('aSup_B12'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(
exclude = c('aAGE'),
include = c('aSup_B12'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.119
## Unadjusted ICC: 0.101
i = i+12.2.40 aVIT_D
Data selection
Open code
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_D))
column_name
## [1] "aVIT_D"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_D +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_D + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 66.392 4.110 16.155 <2e-16 ***
## SEXM 0.103 2.779 0.037 0.9705
## GRPVG 4.886 5.517 0.886 0.3774
## GRPVN 11.269 4.777 2.359 0.0198 *
## aSUP_D 3.179 3.821 0.832 0.4069
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.888 0.348
## s(FAM) 46.16 92 1.021 8.77e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.369 Deviance explained = 54.3%
## GCV = 446.75 Scale est. = 322.15 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_D + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 0.001 0.9705
## GRP 2 2.977 0.0543
## aSUP_D 1 0.692 0.4069
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.888 0.348
## s(FAM) 46.16 92.00 1.021 8.77e-05
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again but with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_D +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_D + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.97256 0.07822 76.355 < 2e-16 ***
## SEXM -0.01146 0.05437 -0.211 0.83338
## GRPVG 0.11505 0.10460 1.100 0.27327
## GRPVN 0.24280 0.09061 2.680 0.00826 **
## aSUP_D 0.08500 0.07310 1.163 0.24695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.837 0.361721
## s(FAM) 42.98 92 0.888 0.000325 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.354 Deviance explained = 52.1%
## GCV = 0.16789 Scale est. = 0.12392 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_D + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 0.044 0.8334
## GRP 2 3.767 0.0255
## aSUP_D 1 1.352 0.2469
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.837 0.361721
## s(FAM) 42.98 92.00 0.888 0.000325
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improved the distribution of residuals. We will continue to work with log-transformed values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_D')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 6.181277836 0.226245837 27.3210677
## GRPVG 0.138166526 0.091275390 1.5137325
## GRPVN 0.242061911 0.079253510 3.0542737
## SEXM -0.015513468 0.063836408 -0.2430191
## aAGE -0.005863583 0.006080554 -0.9643173
## aSUP_D 0.081765675 0.068045424 1.2016337
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on log2(vitamin D) level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 0.2420619 | 0.0867279 | 0.3973959 | 0.0022561 |
| VG vs OM | 0.1381665 | -0.0407300 | 0.3170630 | 0.1300937 |
| VN vs VG | 0.1038954 | -0.0485263 | 0.2563171 | 0.1815583 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_D'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_D'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_D'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_D'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_D'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.300
## Unadjusted ICC: 0.274
i = i+12.2.41 aFOLAT
Data selection
Open code
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_FOL))
column_name
## [1] "aFOLAT"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_FOL +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_FOL + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.0890 0.7935 13.975 < 2e-16 ***
## SEXM -2.2797 0.5395 -4.225 4.47e-05 ***
## GRPVG 3.0293 1.0868 2.787 0.00612 **
## GRPVN 3.2882 0.9421 3.490 0.00066 ***
## aSUP_FOL 3.7571 1.7147 2.191 0.03024 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.949 0.332
## s(FAM) 51.65 92 1.293 7.37e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.495 Deviance explained = 64.9%
## GCV = 17.315 Scale est. = 11.977 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_FOL + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 17.853 4.47e-05
## GRP 2 6.643 0.00179
## aSUP_FOL 1 4.801 0.03024
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.949 0.332
## s(FAM) 51.65 92.00 1.293 7.37e-06
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again but with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_FOL +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_FOL + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.31856 0.09575 34.658 < 2e-16 ***
## SEXM -0.25051 0.06466 -3.875 0.000169 ***
## GRPVG 0.42023 0.13126 3.202 0.001722 **
## GRPVN 0.43708 0.11377 3.842 0.000191 ***
## aSUP_FOL 0.38200 0.20619 1.853 0.066224 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 1.034 0.311
## s(FAM) 52.31 92 1.328 5.45e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.503 Deviance explained = 65.6%
## GCV = 0.24962 Scale est. = 0.17179 n = 187
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_FOL + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 15.012 0.000169
## GRP 2 8.246 0.000427
## aSUP_FOL 1 3.432 0.066224
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 1.034 0.311
## s(FAM) 52.31 92.00 1.328 5.45e-06
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation did not improve the fit substantially. We will continue to work with original values.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_FOL')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 8.74539715 2.32388737 3.7632620
## GRPVG 3.26952370 1.00759759 3.2448705
## GRPVN 3.26659330 0.87406001 3.7372644
## SEXM -2.12938464 0.53158390 -4.0057358
## aAGE 0.05516375 0.06247702 0.8829446
## aSUP_FOL 3.86765482 1.64352693 2.3532653
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on folat level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | 3.2665933 | 1.553467 | 4.979719 | 0.0001860 |
| VG vs OM | 3.2695237 | 1.294669 | 5.244379 | 0.0011750 |
| VN vs VG | -0.0029304 | -1.776008 | 1.770147 | 0.9974154 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_FOL'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_FOL'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_FOL'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_FOL'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_FOL'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.389
## Unadjusted ICC: 0.323
i = i+12.2.42 aUr_Ca
Data selection
Open code
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_FOL))
column_name
## [1] "aUr_Ca"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Ca +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Ca + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.14629 0.22705 5.049 1.29e-06 ***
## SEXM 0.56361 0.18230 3.092 0.00238 **
## GRPVG -0.02652 0.30562 -0.087 0.93096
## GRPVN -0.37643 0.26858 -1.402 0.16314
## aSUP_Ca -0.34816 0.39514 -0.881 0.37970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.133 0.71536
## s(FAM) 32.56 92 0.549 0.00899 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.251 Deviance explained = 40.3%
## GCV = 1.7508 Scale est. = 1.3879 n = 186
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Ca + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 9.559 0.00238
## GRP 2 1.335 0.26633
## aSUP_Ca 1 0.776 0.37970
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.133 0.71536
## s(FAM) 32.56 92.00 0.549 0.00899
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again but with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_Ca +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Ca + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4052 0.2217 -1.828 0.0698 .
## SEXM 0.6698 0.1598 4.192 4.99e-05 ***
## GRPVG -0.1145 0.3032 -0.378 0.7064
## GRPVN -0.4816 0.2657 -1.813 0.0721 .
## aSUP_Ca -0.3184 0.3669 -0.868 0.3871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.667 0.415
## s(FAM) 46.26 92 1.026 8.48e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.39 Deviance explained = 55.9%
## GCV = 1.4529 Scale est. = 1.0446 n = 186
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Ca + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 17.575 4.99e-05
## GRP 2 1.936 0.148
## aSUP_Ca 1 0.753 0.387
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.667 0.415
## s(FAM) 46.26 92.00 1.026 8.48e-05
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation did not improve the fit substantially. We will continue to work with original values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Ca')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 0.28683062 0.74548610 0.3847565
## GRPVG -0.14813426 0.31132407 -0.4758201
## GRPVN -0.51352770 0.27332435 -1.8788216
## SEXM 0.67900088 0.17823988 3.8094780
## aAGE -0.02024741 0.02006671 -1.0090049
## aSUP_Ca -0.27146859 0.39417225 -0.6887055
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on folat level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.5135277 | -1.0492336 | 0.0221782 | 0.0602689 |
| VG vs OM | -0.1481343 | -0.7583182 | 0.4620497 | 0.6342025 |
| VN vs VG | -0.3653934 | -0.9076132 | 0.1768263 | 0.1865717 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Ca'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Ca'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Ca'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Ca'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Ca'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.337
## Unadjusted ICC: 0.305
i = i+12.2.43 aCa_per_Krea
Data selection
Open code
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_FOL))
column_name
## [1] "aCa_per_Krea"
gamm <- gam(
outcome ~
SEX +
GRP +
aSUP_Ca +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Ca + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.23131 0.02478 9.335 <2e-16 ***
## SEXM -0.02651 0.02192 -1.210 0.228
## GRPVG -0.03479 0.03285 -1.059 0.291
## GRPVN -0.04470 0.02885 -1.549 0.123
## aSUP_Ca 0.01612 0.04490 0.359 0.720
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.25 2.818 1.792 0.118
## s(FAM) 16.18 92.000 0.214 0.132
##
## R-sq.(adj) = 0.126 Deviance explained = 23.2%
## GCV = 0.023284 Scale est. = 0.020352 n = 186
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + aSUP_Ca + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 1.463 0.228
## GRP 2 1.228 0.296
## aSUP_Ca 1 0.129 0.720
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.250 2.818 1.792 0.118
## s(FAM) 16.177 92.000 0.214 0.132
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again but with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
aSUP_Ca +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Ca + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.60346 0.16636 -15.650 <2e-16 ***
## SEXM -0.08327 0.13920 -0.598 0.551
## GRPVG -0.11334 0.22359 -0.507 0.613
## GRPVN -0.20240 0.19565 -1.035 0.303
## aSUP_Ca 0.28480 0.29655 0.960 0.338
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.936 3.626 3.263 0.0207 *
## s(FAM) 26.093 92.000 0.401 0.0286 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.211 Deviance explained = 35.2%
## GCV = 0.99101 Scale est. = 0.8097 n = 186
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + aSUP_Ca + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 0.358 0.551
## GRP 2 0.539 0.585
## aSUP_Ca 1 0.922 0.338
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 2.936 3.626 3.263 0.0207
## s(FAM) 26.093 92.000 0.401 0.0286
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation did not improve the fit substantially. We will continue to work with original values.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult',
include = c('aSUP_FOL')),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) -3.374568041 0.53164345 -6.34742708
## GRPVG -0.007476637 0.20401757 -0.03664703
## GRPVN -0.162625429 0.17832141 -0.91197925
## SEXM -0.102905332 0.15036190 -0.68438434
## aAGE 0.021788649 0.01432958 1.52053596
## aSUP_FOL -0.008522708 0.38588646 -0.02208605
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on folat level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.1626254 | -0.5121290 | 0.1868781 | 0.3617796 |
| VG vs OM | -0.0074766 | -0.4073437 | 0.3923905 | 0.9707664 |
| VN vs VG | -0.1551488 | -0.5145968 | 0.2042993 | 0.3975636 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult',
include = c('aSUP_Ca'))
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult',
include = c('aSUP_Ca'))
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
include = c('aSUP_Ca'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
include = c('aSUP_Ca'),
type = 'adult')
### model without other
mod_other_cov <- rlme(type = 'adult')
### model without age
mod_age <- rlme(exclude = c('aAGE'),
include = c('aSUP_Ca'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.137
## Unadjusted ICC: 0.134
i = i+12.2.44 aI_per_Krea
Data selection
Open code
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_FOL))
column_name
## [1] "aI_per_Krea"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 376.40 83.02 4.534 1.21e-05 ***
## SEXM -219.08 69.90 -3.134 0.00209 **
## GRPVG -89.27 109.14 -0.818 0.41476
## GRPVN 64.66 94.66 0.683 0.49562
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 1.012 0.3161
## s(FAM) 18.66 84 0.289 0.0852 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.165 Deviance explained = 27.8%
## GCV = 2.1634e+05 Scale est. = 1.8587e+05 n = 168
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 9.824 0.00209
## GRP 2 1.328 0.26828
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 1.012 0.3161
## s(FAM) 18.66 84.00 0.289 0.0852
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again but with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.8474 0.3092 25.381 < 2e-16 ***
## SEXM -0.7862 0.1965 -4.000 0.000115 ***
## GRPVG -0.6744 0.4256 -1.585 0.115874
## GRPVN -0.2142 0.3657 -0.586 0.559323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.813 2.195 3.035 0.0484 *
## s(FAM) 51.232 84.000 1.604 1.64e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.485 Deviance explained = 65.8%
## GCV = 2.0915 Scale est. = 1.3813 n = 168
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 16.001 0.000115
## GRP 2 1.329 0.268818
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.813 2.195 3.035 0.0484
## s(FAM) 51.232 84.000 1.604 1.64e-06
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation improve the fit substantially. Lets continue with log-scale.
Fit main model
Open code
dat_mod$outcome <- log2(dat_mod$outcome)
column_name <- paste0("log2_", column_name)
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 6.00018133 0.89390181 6.7123494
## GRPVG -0.66504463 0.37191007 -1.7881867
## GRPVN -0.29407755 0.32107670 -0.9159106
## SEXM -0.65835244 0.20708806 -3.1790941
## aAGE 0.05122287 0.02386459 2.1463961
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on folat level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.2940775 | -0.9233763 | 0.3352212 | 0.3597138 |
| VG vs OM | -0.6650446 | -1.3939750 | 0.0638857 | 0.0737459 |
| VN vs VG | 0.3709671 | -0.2629125 | 1.0048466 | 0.2513671 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE); icc(mod_main); diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.432
## Unadjusted ICC: 0.393
i = i+12.2.45 aP_per_Krea
Data selection
Open code
column_name <- names(dat_adult)[i+ni]
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome),
!is.na(aSUP_FOL))
column_name
## [1] "aP_per_Krea"
gamm <- gam(
outcome ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0693 0.1303 15.882 < 2e-16 ***
## SEXM -0.4232 0.1009 -4.194 4.77e-05 ***
## GRPVG -0.3227 0.1757 -1.837 0.0683 .
## GRPVN -0.6502 0.1524 -4.266 3.60e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1 0.094 0.75965
## s(FAM) 37.29 92 0.686 0.00233 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.376 Deviance explained = 51.5%
## GCV = 0.55163 Scale est. = 0.42622 n = 186
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## outcome ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 17.591 4.77e-05
## GRP 2 9.299 0.000159
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.00 1.00 0.094 0.75965
## s(FAM) 37.29 92.00 0.686 0.00233
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Again but with log2-transformed outcome
Open code
gamm <- gam(
log2(outcome) ~
SEX +
GRP +
s(aAGE) +
s(FAM, bs="re"),
data = dat_mod)
summary(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0055 0.1391 7.227 1.91e-11 ***
## SEXM -0.5439 0.1196 -4.547 1.07e-05 ***
## GRPVG -0.3867 0.1841 -2.101 0.0372 *
## GRPVN -0.7098 0.1599 -4.438 1.68e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.19 1.337 0.736 0.5249
## s(FAM) 20.94 92.000 0.296 0.0735 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.283 Deviance explained = 38%
## GCV = 0.70981 Scale est. = 0.61009 n = 186
anova(gamm)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## log2(outcome) ~ SEX + GRP + s(aAGE) + s(FAM, bs = "re")
##
## Parametric Terms:
## df F p-value
## SEX 1 20.675 1.07e-05
## GRP 2 9.941 8.52e-05
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(aAGE) 1.190 1.337 0.736 0.5249
## s(FAM) 20.939 92.000 0.296 0.0735
plot(gamm, select = 1)
Open code
### Model check
pltmd(gamm)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Log-transformation did not improve the fit substantially. We will continue to work with original values.
Fit main model
Open code
## main model
mod_main <- run(
rlme(main = TRUE,
remove_random = FALSE,
type = 'adult'),
path = paste0('gitignore/run/mod_adult_', column_name, '_main_mixef'),
reuse = TRUE)
summary(mod_main)[['coefficients']]
## Estimate Std. Error t value
## (Intercept) 1.906848881 0.39557989 4.8203888
## GRPVG -0.349824498 0.15170977 -2.3058798
## GRPVN -0.624859463 0.13205042 -4.7319763
## SEXM -0.447671905 0.11152229 -4.0141922
## aAGE 0.003239738 0.01066292 0.3038322
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on folat level, estimated with a robust linear mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped',full_width = T) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.6248595 | -0.8836735 | -0.3660454 | 0.0000022 |
| VG vs OM | -0.3498245 | -0.6471702 | -0.0524788 | 0.0211173 |
| VN vs VG | -0.2750350 | -0.5399325 | -0.0101374 | 0.0418539 |
Leave-one-factor mixed models
Open code
### main model but non-robust
mod_main <- rlme(
remove_random = FALSE,
type = 'adult')
res2 <- emm(mod_main)
### model without random effect
mod_nonran <- rlme(
remove_random = TRUE,
type = 'adult')
### model without sex
mod_nsex <- rlme(exclude = c('SEX'),
type = 'adult')
### model without diet groups
mod_ndiet <- rlme(exclude = c('GRP'),
type = 'adult')
### model without other
mod_other_cov <- NA
### model without age
mod_age <- rlme(exclude = c('aAGE'),
type = 'adult')Putting key results together
Open code
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE)
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.249
## Unadjusted ICC: 0.205
diet_adult_non_robust <- add_eff(diet_adult_non_robust, res2, mixef = TRUE)
i = i+12.2.46 aAL_adult
Data selection
Open code
column_name <- names(dat_adult)[i+ni]
column_name
## [1] "aAL_adult"
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))Main model
Open code
mod_main <- glmer(
outcome ~
SEX +
GRP +
aAGE +
(1|FAM),
family = binomial(link = 'logit'),
data = dat_mod)
summary(mod_main)[['coefficients']]
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.59639357 2.01758195 0.2955982 0.76753696
## SEXM -0.89810720 0.49317267 -1.8210806 0.06859460
## GRPVG -0.60620973 0.79589327 -0.7616721 0.44625570
## GRPVN -1.94514535 0.81526576 -2.3859034 0.01703723
## aAGE -0.03281529 0.05567048 -0.5894559 0.55555549
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on the likelihood of food allergy, estimated with a logistic mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped', full_width = TRUE) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -1.9451453 | -3.543037 | -0.3472538 | 0.0170372 |
| VG vs OM | -0.6062097 | -2.166132 | 0.9537124 | 0.4462557 |
| VN vs VG | -1.3389356 | -2.872539 | 0.1946679 | 0.0870486 |
Leave-one-factor mixed models
Open code
### model without random effect
mod_nonran <- glm(
outcome ~
SEX +
GRP +
aAGE,
family = binomial(link = 'logit'),
data = dat_mod)
### model without sex
mod_nsex <- glmer(
outcome ~
GRP +
aAGE +
(1|FAM),
family = binomial(link = 'logit'),
data = dat_mod)
### model without diet groups
mod_ndiet <- glmer(
outcome ~
SEX +
aAGE +
(1|FAM),
family = binomial(link = 'logit'),
data = dat_mod)
### model without other covariates
mod_other_cov <- NA
### model without log2_age
mod_age <- glmer(
outcome ~
SEX +
GRP +
(1|FAM),
family = binomial(link = 'logit'),
data = dat_mod)Putting key results together
Open code
## AIC
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE)
diet_adult_non_robust <- add_eff(diet_adult_non_robust, res, mixef = TRUE)
diet_adult$estimand[i+1] <- 'log(OR)'
diet_adult_non_robust$estimand[i+1] <- 'log(OR)'
icc(mod_main)
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.479
## Unadjusted ICC: 0.417
i = i+12.2.47 aBREAKS
Data selection
Open code
column_name <- 'aBREAKS'
column_name
## [1] "aBREAKS"
dat_mod <- dat_adult %>%
mutate(outcome = !!sym(column_name)) %>%
filter(!is.na(outcome))Main model
Open code
mod_main <- glmer(
outcome ~
SEX +
GRP +
aAGE +
(1|FAM),
family = binomial(link = 'logit'),
data = dat_mod)
## boundary (singular) fit: see help('isSingular')
summary(mod_main)[['coefficients']]
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.58183854 1.14349267 0.5088258 6.108744e-01
## SEXM 1.57121600 0.33270878 4.7224964 2.329673e-06
## GRPVG -0.11966042 0.44060054 -0.2715848 7.859413e-01
## GRPVN -0.68932448 0.38775435 -1.7777350 7.544739e-02
## aAGE -0.03230162 0.03105942 -1.0399942 2.983426e-01
res <- emm(mod_main)
suppl_table <- kbl(res, caption =
'The effect of diet on the likelihood of having experience of bone break, estimated with a logistic mixed-effects model in adults. CI_L and CI_U are bounds of 95% confidence interval') %>%
kable_styling('striped', full_width = TRUE) %>%
column_spec(1, width_min = '1in')
suppl_table| Estimate | CI-L | CI-U | P | |
|---|---|---|---|---|
| VN vs OM | -0.6893245 | -1.4493090 | 0.0706601 | 0.0754474 |
| VG vs OM | -0.1196604 | -0.9832216 | 0.7439008 | 0.7859413 |
| VN vs VG | -0.5696641 | -1.3513739 | 0.2120458 | 0.1532037 |
Leave-one-factor mixed models
Open code
### model without random effect
mod_nonran <- glm(
outcome ~
SEX +
GRP +
aAGE,
family = binomial(link = 'logit'),
data = dat_mod)
### model without sex
mod_nsex <- glmer(
outcome ~
GRP +
aAGE +
(1|FAM),
family = binomial(link = 'logit'),
data = dat_mod)
### model without diet groups
mod_ndiet <- glmer(
outcome ~
SEX +
aAGE +
(1|FAM),
family = binomial(link = 'logit'),
data = dat_mod)
### model without other covariates
mod_other_cov <- NA
### model without log2_age
mod_age <- glmer(
outcome ~
SEX +
GRP +
(1|FAM),
family = binomial(link = 'logit'),
data = dat_mod)Putting key results together
Open code
## AIC
AIC_adult <- add_AIC_adult(AIC_adult, mixef = TRUE)
diet_adult <- add_eff(diet_adult, res, mixef = TRUE)
diet_adult_non_robust <- add_eff(diet_adult_non_robust, res, mixef = TRUE)
diet_adult$estimand[i+1] <- 'log(OR)'
diet_adult_non_robust$estimand[i+1] <- 'log(OR)'
icc(mod_main)
## boundary (singular) fit: see help('isSingular')
## Warning: Can't compute random effect variances. Some variance components equal
## zero. Your model may suffer from singularity (see `?lme4::isSingular`
## and `?performance::check_singularity`).
## Solution: Respecify random structure! You may also decrease the
## `tolerance` level to enforce the calculation of random effect variances.
## [1] NA2.2.48 Saving tables
Open code
## Between-diets differences
if(file.exists('gitignore/data/diet_adult_mixeff.xlsx') == FALSE){
diet_adult <- diet_adult[-1,]
diet_adult$VN_OM_P_adj <- p.adjust(diet_adult$VN_OM_P, method = 'fdr')
diet_adult$VG_OM_P_adj <- p.adjust(diet_adult$VG_OM_P, method = 'fdr')
diet_adult$VN_VG_P_adj <- p.adjust(diet_adult$VN_VG_P, method = 'fdr')
# for (i in seq(1, nrow(diet_adult))) {
# padj <- p.adjust(c(
# diet_adult$VN_OM_P[i],
# diet_adult$VG_OM_P[i],
# diet_adult$VN_VG_P[i]),
# method = 'hochberg')
#
# diet_adult$VN_OM_P_adj[i] <- padj[1]
# diet_adult$VG_OM_P_adj[i] <- padj[2]
# diet_adult$VN_VG_P_adj[i] <- padj[3]
# }
diet_adult <- diet_adult %>%
select(outcome, estimand,
VN_OM_diff, VN_OM_P, VN_OM_P_adj,
VG_OM_diff, VG_OM_P, VG_OM_P_adj,
VN_VG_diff, VN_VG_P, VN_VG_P_adj) %>%
mutate(across(.cols = 3:11, .fns = ~as.numeric(as.character(.)))) %>%
mutate(across(.cols = c(VN_OM_P, VG_OM_P, VN_VG_P,
VN_OM_P_adj, VG_OM_P_adj, VN_VG_P_adj), ~ round(.x, 5)))
write.xlsx(diet_adult, 'gitignore/data/diet_adult_mixeff.xlsx')
}
if(file.exists('gitignore/data/diet_adult_non_robust_mixeff_non_robust.xlsx') == FALSE){
diet_adult_non_robust <- diet_adult_non_robust[-1,]
diet_adult_non_robust$VN_OM_P_adj <- p.adjust(
diet_adult_non_robust$VN_OM_P, method = 'fdr')
diet_adult_non_robust$VG_OM_P_adj <- p.adjust(
diet_adult_non_robust$VG_OM_P, method = 'fdr')
diet_adult_non_robust$VN_VG_P_adj <- p.adjust(
diet_adult_non_robust$VN_VG_P, method = 'fdr')
# for (i in seq(1, nrow(diet_adult_non_robust))) {
# padj <- p.adjust(c(
# diet_adult_non_robust$VN_OM_P[i],
# diet_adult_non_robust$VG_OM_P[i],
# diet_adult_non_robust$VN_VG_P[i]),
# method = 'hochberg')
#
# diet_adult_non_robust$VN_OM_P_adj[i] <- padj[1]
# diet_adult_non_robust$VG_OM_P_adj[i] <- padj[2]
# diet_adult_non_robust$VN_VG_P_adj[i] <- padj[3]
# }
diet_adult_non_robust <- diet_adult_non_robust %>%
select(outcome, estimand,
VN_OM_diff, VN_OM_P, VN_OM_P_adj,
VG_OM_diff, VG_OM_P, VG_OM_P_adj,
VN_VG_diff, VN_VG_P, VN_VG_P_adj) %>%
mutate(across(.cols = 3:11, .fns = ~as.numeric(as.character(.)))) %>%
mutate(across(.cols = c(VN_OM_P, VG_OM_P, VN_VG_P,
VN_OM_P_adj, VG_OM_P_adj, VN_VG_P_adj), ~ round(.x, 5)))
write.xlsx(diet_adult_non_robust, 'gitignore/data/diet_adult_non_robust_mixeff_non_robust.xlsx')
}
## Importance of predictors
if(file.exists('gitignore/data/AIC_adult_mixeff.xlsx') == FALSE){
AIC_adult <- AIC_adult[-1,]
write.xlsx(AIC_adult, 'gitignore/data/AIC_adult_mixeff.xlsx')
}3 Reproducibility
Open code
sessionInfo()
## R version 4.4.3 (2025-02-28)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=cs_CZ.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=cs_CZ.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=cs_CZ.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=cs_CZ.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Prague
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] mice_3.17.0 patchwork_1.2.0 ggrepel_0.9.5 robustlmm_3.3-1
## [5] gridExtra_2.3 pheatmap_1.0.12 performance_0.12.2 quantreg_5.98
## [9] SparseM_1.81 bayesplot_1.8.1 ggdist_3.3.2 kableExtra_1.4.0
## [13] lubridate_1.8.0 corrplot_0.92 arm_1.12-2 MASS_7.3-64
## [17] projpred_2.0.2 glmnet_4.1-8 boot_1.3-31 cowplot_1.1.1
## [21] pROC_1.18.0 mgcv_1.9-1 nlme_3.1-167 openxlsx_4.2.5
## [25] flextable_0.9.6 sjPlot_2.8.16 car_3.1-2 carData_3.0-5
## [29] gtsummary_2.0.2 emmeans_1.10.4 ggpubr_0.4.0 lme4_1.1-35.5
## [33] Matrix_1.7-0 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
## [37] purrr_1.0.2 readr_2.1.2 tidyr_1.3.1 tibble_3.2.1
## [41] ggplot2_3.5.1 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] splines_4.4.3 later_1.3.0 gamm4_0.2-6
## [4] cellranger_1.1.0 datawizard_0.12.2 rpart_4.1.24
## [7] reprex_2.0.1 lifecycle_1.0.4 rstatix_0.7.0
## [10] lattice_0.22-5 insight_0.20.2 backports_1.5.0
## [13] magrittr_2.0.3 rmarkdown_2.27 yaml_2.3.5
## [16] httpuv_1.6.5 zip_2.2.0 askpass_1.1
## [19] DBI_1.1.2 minqa_1.2.4 RColorBrewer_1.1-2
## [22] multcomp_1.4-18 abind_1.4-5 rvest_1.0.2
## [25] nnet_7.3-20 TH.data_1.1-0 sandwich_3.0-1
## [28] gdtools_0.3.7 pbkrtest_0.5.1 crul_1.5.0
## [31] MatrixModels_0.5-3 svglite_2.1.3 codetools_0.2-19
## [34] xml2_1.3.3 tidyselect_1.2.1 shape_1.4.6
## [37] farver_2.1.0 ggeffects_1.7.0 httpcode_0.3.0
## [40] matrixStats_1.3.0 jsonlite_1.8.8 mitml_0.4-3
## [43] ellipsis_0.3.2 ggridges_0.5.3 survival_3.7-0
## [46] iterators_1.0.14 systemfonts_1.0.4 foreach_1.5.2
## [49] tools_4.4.3 ragg_1.2.1 Rcpp_1.0.13
## [52] glue_1.7.0 pan_1.6 xfun_0.46
## [55] distributional_0.4.0 loo_2.4.1 withr_3.0.1
## [58] fastmap_1.2.0 fansi_1.0.6 openssl_1.4.6
## [61] digest_0.6.37 R6_2.5.1 mime_0.12
## [64] estimability_1.5.1 textshaping_0.3.6 colorspace_2.0-2
## [67] utf8_1.2.4 generics_0.1.3 fontLiberation_0.1.0
## [70] data.table_1.15.4 robustbase_0.93-9 httr_1.4.2
## [73] htmlwidgets_1.6.4 pkgconfig_2.0.3 gtable_0.3.0
## [76] htmltools_0.5.8.1 fontBitstreamVera_0.1.1 scales_1.3.0
## [79] knitr_1.48 rstudioapi_0.16.0 tzdb_0.2.0
## [82] uuid_1.0-3 coda_0.19-4 curl_4.3.2
## [85] nloptr_2.0.0 zoo_1.8-9 sjlabelled_1.2.0
## [88] parallel_4.4.3 pillar_1.9.0 vctrs_0.6.5
## [91] promises_1.2.0.1 jomo_2.7-3 dbplyr_2.1.1
## [94] xtable_1.8-4 evaluate_1.0.0 fastGHQuad_1.0.1
## [97] mvtnorm_1.1-3 cli_3.6.3 compiler_4.4.3
## [100] rlang_1.1.4 crayon_1.5.0 rstantools_2.1.1
## [103] ggsignif_0.6.3 labeling_0.4.2 modelr_0.1.8
## [106] plyr_1.8.6 sjmisc_2.8.10 fs_1.6.4
## [109] stringi_1.7.6 viridisLite_0.4.0 assertthat_0.2.1
## [112] munsell_0.5.0 fontquiver_0.2.1 sjstats_0.19.0
## [115] hms_1.1.1 gfonts_0.2.0 shiny_1.9.1
## [118] highr_0.11 haven_2.4.3 broom_1.0.6
## [121] DEoptimR_1.0-10 readxl_1.3.1 officer_0.6.6